Data processing method and communication apparatus

Abstract

The present application provides a data processing method and a communication apparatus. The method comprises: a second device sending a first message to a first device, wherein the first message comprises one or more of communication condition information of the second device, computing resource information of the second device, or model training information of the second device; the second device receiving a second message from the first device, wherein the second message comprises indication information, the indication information indicates a quantization resolution, and the quantization resolution is related to one or more of the communication condition information, the computing resource information, or the model training information; furthermore, the second device quantizing first artificial intelligence (AI) collaboration data on the basis of quantization resolution to obtain second AI collaboration data; and sending the second AI collaboration data to the first device. By means of the method, the second device can quantize AI collaboration data by means of a dynamically changing quantization resolution, such that the training efficiency of federated learning is improved.

Description

Data processing method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on September 25, 2023, with application number 202311246336.1 and application name “A Data Processing Method and Communication Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and in particular to a data processing method and a communication device. Background Art

[0003] Federated learning is a distributed machine learning paradigm that enables multiple devices to jointly train and build shared machine learning models without sharing data resources. Specifically, during the federated learning process, participating distributed nodes can jointly train by sharing artificial intelligence (AI)-assisted data.

[0004] In order to reduce the communication overhead generated by each distributed node uploading and downloading AI-assisted data during the federated learning process, the AI-assisted data can usually be compressed in a quantized manner. In other words, the AI-assisted data is represented by multiple bits, and the number of these bits can determine the quantization level (or quantization resolution), thereby affecting the performance of the gradient quantization algorithm. Among them, the fewer bits used by the quantization algorithm, the lower the quantization resolution, the greater the quantization error introduced during the AI-assisted data upload process, and the longer the model convergence time; the more bits used by the quantization algorithm, the higher the quantization resolution, and the longer the communication time for transmitting the AI-assisted data.

[0005] In the process of federated learning, the quantization resolution AI used by distributed nodes to assist in data quantization needs further research.

[0006] Summary of the Invention

[0007] The embodiments of the present application provide a data processing method and a communication device, in which the second device can quantize AI collaborative data through dynamically changing quantization resolution, which is conducive to improving the training efficiency of collaborative learning.

[0008] In a first aspect, the present application provides a data processing method. Taking the execution of the method by a second device as an example, the method includes: the second device sends a first message to the first device, and the first message includes one or more of the communication condition information of the second device, the computing resource information of the second device, or the model training information of the second device; the second device receives a second message from the first device, and the second message includes indication information, and the indication information indicates a quantization resolution, and the quantization resolution is related to one or more of the communication condition information, the computing resource information, or the model training information; further, the second device quantizes the first artificial intelligence (AI) collaborative data based on the quantization resolution to obtain second AI collaborative data; and sends the second AI collaborative data to the first device.

[0009] Based on the method described in the first aspect, combined with the communication conditions, computing resources or model training information of the current second device, the quantization resolution of the second device is dynamically adjusted, which is conducive to improving the adaptability of the quantization resolution to the current second device. By quantizing and transmitting AI collaborative data at a quantization resolution adapted to the current second device, it is conducive to improving the training efficiency of collaborative learning.

[0010] In one possible implementation, the communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal received power RSRP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, bit error rate or throughput; the computing resource information includes one or more of the size of computing resources, the size of computing power or computing delay; the model training information includes one or more of the test set loss value, the training set loss value, the gradient norm size, the gradient size, and the model size.

[0011] In one possible implementation, the second device receives a third message from the first device requesting assistance in updating the quantization resolution; the first message is a response message to the third message. This possible implementation improves the flexibility of triggering the second device to send the first message.

[0012] In one possible implementation, the third message includes one or more of the following information: a measurement identifier, an identifier of the second device, an identifier of the first device, an identifier of the model, indication information indicating the reason for updating the quantization resolution, an identifier of the training wheel corresponding to the model, an identifier of the information requested for feedback, or configuration information for transmitting the first message; wherein the information requested for feedback includes one or more of communication condition information, computing resource information, or model training information; and the first message is generated based on the identifier of the information requested for feedback.

[0013] In one possible implementation, the indication of the reason for updating the quantization resolution includes the time difference between a first delay and an average delay, or the ratio between the first delay and the average delay; the first delay is the delay between the first device waiting for the second device to feedback AI collaboration data, and the average delay is the average of the delays between the first device waiting for multiple distributed nodes, including the second device, to feedback AI collaboration data. This possible implementation method helps shorten the delay in the second device feedbacking AT assistance data, thereby improving the training efficiency of collaborative learning.

[0014] In one possible implementation, the configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a trigger condition for transmitting the first message, or a number of times the first message is transmitted. By implementing this possible implementation, the second device can automatically send the first message to the first device based on the configuration information to assist in updating the quantization resolution, thereby conserving communication transmission resources.

[0015] In a possible implementation manner, the first message further includes one or more of the following information: a measurement identifier, a second device identifier, a first device identifier, an identifier of the model, and an identifier of a training wheel corresponding to the model.

[0016] In the second aspect, the present application provides a data processing method. Taking the first device executing the method as an example, the method includes: the first device receives a first message from a second device, and the first message includes one or more of the communication condition information of the second device, the computing resource information of the second device, or the model training information of the second device; further, based on one or more of the communication condition information, the computing resource information, or the model training information, the quantization resolution is determined; and a second message is sent to the second device, and the second message includes indication information, and the indication information indicates the quantization resolution, and the quantization resolution is related to one or more of the communication condition information, the computing resource information, or the model training information; the first device receives second artificial intelligence AI collaboration data from the second device, and the second AI collaboration data is obtained by quantizing the first AI collaboration data based on the quantization resolution.

[0017] For the beneficial effects obtained by the method described in the second aspect, reference may be made to the method and beneficial effects described in the first aspect above.

[0018] In one possible implementation, the communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal received power RSRP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, bit error rate or throughput; the computing resource information includes one or more of the size of computing resources, the size of computing power or computing delay; the model training information includes one or more of the test set loss value, the training set loss value, the gradient norm size, the gradient size, and the model size.

[0019] In a possible implementation, the first device sends a third message to the second device, where the third message is used to request assistance in updating the quantization resolution; and the aforementioned first message is a response message corresponding to the third message.

[0020] In one possible implementation, the third message includes one or more of the following information: a measurement identifier, an identifier of the second device, an identifier of the first device, a model identifier, indication information indicating the reason for updating the quantization resolution, an identifier of the training wheel corresponding to the model, an identifier of the information requested for feedback, and / or configuration information for transmitting the first message; wherein the information requested for feedback includes one or more of communication condition information, computing resource information, or model training information; and the first message is generated based on the identifier of the information requested for feedback.

[0021] In one possible implementation, the indication information indicating the reason for updating the quantization resolution includes a time difference between a first delay and an average delay, or a ratio between the first delay and the average delay; wherein the first delay is the delay of the first device waiting for the second device to feedback the AI ​​collaboration data, and the average delay is the average value of the delays of the first device waiting for multiple distributed nodes to feedback the AI ​​collaboration data, where the multiple distributed nodes include the second device.

[0022] In a possible implementation, the configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a triggering condition for transmitting the first message, or the number of times the first message is transmitted.

[0023] In a possible implementation manner, the first message further includes one or more of the following information: a measurement identifier, a second device identifier, a first device identifier, an identifier of the model, and an identifier of a training wheel corresponding to the model.

[0024] In a third aspect, the present application provides a communication device, which may be a second device, a device in the second device, or a device that can be used in conjunction with the second device. The communication device may also be a chip system. The communication device may execute the method described in the first aspect. The functions of the communication device may be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more units or modules corresponding to the above functions. The unit or module may be software and / or hardware. The operations and beneficial effects performed by the communication device may refer to the method and beneficial effects described in the first aspect above.

[0025] In a fourth aspect, the present application provides a communication device, which may be a first device, a device in the first device, or a device that can be used in conjunction with the first device. The communication device may also be a chip system. The communication device may execute the method described in the second aspect. The functions of the communication device may be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more units or modules corresponding to the above functions. The units or modules may be software and / or hardware. The operations and beneficial effects performed by the communication device may refer to the method and beneficial effects described in the second aspect above.

[0026] In a fifth aspect, the present application provides a communication device, which includes a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices outside the communication device and transmit them to the processor or send signals from the processor to other communication devices outside the communication device, and the processor is used to implement the method as described in the first aspect through a logic circuit or executing code instructions, or the processor is used to implement the method as described in the second aspect through a logic circuit or executing code instructions.

[0027] In a sixth aspect, the present application provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a communication device, the method described in the first aspect or the method described in the second aspect is implemented.

[0028] In a seventh aspect, the present application provides a computer program product comprising instructions, which, when a communication device reads and executes the instructions, causes the communication device to execute the method as described in the first aspect, or causes the communication device to execute the method as described in the second aspect.

[0029] In an eighth aspect, the present application provides a communication system, comprising a communication device for executing the method described in the first aspect above, and a communication device for executing the method executed by the network device described in the second aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIG1 is a schematic diagram of a communication system provided in an embodiment of the present application;

[0031] FIG2 is a schematic diagram of an application framework of AI in an NR system provided by an embodiment of the present application;

[0032] FIG3 is a schematic diagram of a process of federated learning provided in an embodiment of the present application;

[0033] FIG4a is a flow chart of a data processing method provided in an embodiment of the present application;

[0034] FIG4 b is a schematic diagram of a collaborative learning process provided by an embodiment of the present application;

[0035] FIG4c is a schematic diagram of another collaborative learning process provided by an embodiment of the present application;

[0036] FIG5 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0037] FIG6 is a schematic structural diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to facilitate a detailed understanding of the embodiments of the present application, the system architecture involved in the embodiments of the present application is first introduced below.

[0039] Figure 1 is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application. As shown in Figure 1, the communication system includes a radio access network (RAN) 100 and a core network 200. Optionally, the communication system 1000 may also include the Internet 300. The RAN 100 includes at least one RAN node (e.g., 110a and 110b in Figure 1, collectively referred to as 110) and may also include at least one terminal (e.g., 120a-120j in Figure 1, collectively referred to as 120). The RAN 100 may also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in Figure 1). The terminal 120 is wirelessly connected to the RAN node 110, and the RAN node 110 is wirelessly or wiredly connected to the core network 200. The core network devices in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or they may be the same physical device that integrates the logical functions of the core network devices and the logical functions of the RAN nodes. Terminals and RAN nodes may be connected to each other via wired or wireless means. It should be noted that the RAN node 110 may also be referred to as a network device 110 in the following text.

[0040] RAN100 may be an evolved universal terrestrial radio access (E-UTRA) system, a new radio (NR) system, or a future radio access system defined in the 3rd Generation Partnership Project (3GPP). RAN100 may also include two or more of the aforementioned different radio access systems. RAN100 may also be an open RAN (O-RAN).

[0041] A RAN node, also known as a radio access network device, RAN entity, or access node, facilitates wireless access to a communication system by a terminal. In one application scenario, a RAN node can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a fifth-generation (5G) mobile communication system, a next-generation base station in a sixth-generation (6G) mobile communication system, or a base station in a future mobile communication system. A RAN node can be a macro base station (such as 110a in Figure 1), a micro base station, an indoor station (such as 110b in Figure 1), a relay node, or a donor node.

[0042] In another application scenario, multiple RAN nodes can collaborate to help terminals achieve wireless access, with different RAN nodes implementing portions of the base station's functions. For example, a RAN node can be a centralized unit (CU), a distributed unit (DU), or a radio unit (RU). The CU implements the base station's radio resource control protocol and packet data convergence protocol (PDCP) functions, as well as the service data adaptation protocol (SDAP) functions. The DU implements the base station's radio link control layer and medium access control (MAC) layer functions, as well as some or all of the physical layer functions. For detailed descriptions of each of the above protocol layers, please refer to the relevant 3GPP technical specifications. The RU can be used to implement the transmission and reception of radio frequency signals. The CU and DU can be two independent RAN nodes, or they can be integrated into the same RAN node, such as in a baseband unit (BBU). The RU can be included in radio frequency equipment, such as a remote radio unit (RRU) or an active antenna unit (AAU). The CU can be further divided into two types of RAN nodes: CU-control plane and CU-user plane.

[0043] In different systems, RAN nodes may have different names. For example, in an O-RAN system, a CU may be called an open CU (O-CU), a DU may be called an open DU (O-DU), and a RU may be called an open RU (O-RU). The RAN node in the embodiments of the present application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module. For example, the RAN node may be a server loaded with the corresponding software module. The embodiments of the present application do not limit the specific technology and specific device form adopted by the RAN node. For ease of description, the following description takes a base station as an example of a RAN node.

[0044] A terminal is a device with wireless transceiver capabilities that can send signals to or receive signals from a base station. A terminal may also be referred to as a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, etc. A terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, aircraft, ship, robot, robotic arm, smart home device, etc. The embodiments of this application do not limit the specific technology and specific device form used by the terminal.

[0045] Base stations and terminals can be fixed or mobile. They can be deployed on land, indoors or outdoors, handheld or vehicle-mounted; on water; or on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of base stations and terminals.

[0046] The roles of base stations and terminals can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile base station. To terminals 120j accessing the wireless access network 100 via 120i, terminal 120i is a base station. However, to base station 110a, 120i is a terminal, meaning that communication between 110a and 120i occurs via a wireless air interface protocol. Of course, communication between 110a and 120i can also occur via a base station-to-base station interface protocol. In this case, 120i is also a base station relative to 110a. Therefore, base stations and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 1 can be referred to as communication devices with base station functionality, while 120a-120j in Figure 1 can be referred to as communication devices with terminal functionality.

[0047] Communication between base stations and terminals, between base stations, and between terminals can be carried out through authorized spectrum, unauthorized spectrum, or both; communication can be carried out through spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or spectrum below 6 GHz and spectrum above 6 GHz. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0048] In the embodiments of the present application, the functions of the base station may also be performed by a module (such as a chip) in the base station, or by a control subsystem that includes the base station functions. The control subsystem that includes the base station functions here may be a control center in the above-mentioned application scenarios such as smart grid, industrial control, smart transportation, and smart city. The functions of the terminal may also be performed by a module (such as a chip or modem) in the terminal, or by a device that includes the terminal functions.

[0049] In this application, a base station sends downlink signals or downlink information to a terminal, and the downlink information is carried on a downlink channel; the terminal sends uplink signals or uplink information to the base station, and the uplink information is carried on an uplink channel. In order to communicate with the base station, the terminal needs to establish a wireless connection with the cell controlled by the base station. The cell with which the terminal has established a wireless connection is called the serving cell of the terminal. When the terminal communicates with the serving cell, it will also be subject to interference from signals in neighboring cells.

[0050] In order to facilitate understanding of the relevant contents of the embodiments of the present application, some of the terms involved in the embodiments of the present application are explained below. This part is only for ease of understanding and cannot be regarded as a disclosure or specific limitation of the technical solution of the present application.

[0051] 1. AI

[0052] AI, a technology that simulates the human brain to perform complex calculations, can be applied to NR systems. Specifically, it can improve network performance and user experience by intelligently collecting and analyzing data. Figure 2 shows the AI ​​application framework in NR systems.

[0053] In Figure 2, the data collection module serves as a database for AI model training and data analysis and inference, collecting and storing data inputs from, for example, the gNB, gNB-CU, gNB-DU, UE, or other network entities. The model training module analyzes the training data provided by the data collection module to obtain an optimal AI model. The model inference module, based on the AI ​​model generated by the model training module, analyzes the prediction data provided by the data collection module and outputs prediction results (including reasonable predictions about network operation or guidance on implementing appropriate adjustment policies) to the actor. Furthermore, the actor schedules and manages network entities based on the prediction results output by the model prediction module. Specifically, the actor schedules multiple network entities, including itself, to execute operations corresponding to the prediction results. After each network entity in the communication network executes the corresponding operations based on the prediction results, the specific performance of the communication network (which can be understood as the data generated during the operation of each network entity) is collected and stored by the data collection module.

[0054] 2. Federated Learning

[0055] Federated learning involves the joint training of AI models by multiple devices (referred to as participating distributed nodes) without sharing data resources (or, more accurately, the model's training data) to build a shared AI model. Based on the distribution of training data across distributed nodes in the data feature space and sample identity (ID) space, federated learning can be categorized into three types: horizontal federated learning (HFL), vertical federated learning (VFL), and federated transfer learning (FTL).

[0056] Regardless of the type of federated learning, participating distributed nodes can jointly train AI models by sharing AI collaborative data instead of shared data resources. AI collaborative data includes one or more of the following: gradient data, AI models, AI sub-models, AI model outputs, and AI model intermediate values.

[0057] Exemplarily, as shown in FIG3 , device 1 and device 2 are distributed nodes participating in federated learning. During the federated learning process, device 1 performs model training to obtain AI collaboration data #1, and device 2 performs model training to obtain AI collaboration data #2. Furthermore, device 1 sends AI collaboration data #1 to the centralized node (server) of the federated learning, and device 2 sends AI collaboration data #2 to the centralized node. The centralized node aggregates the AI ​​collaboration data #1 and AI collaboration data #2 to obtain AI collaboration data #12. Afterwards, the centralized node sends the AI ​​collaboration data #12 to device 1 and device 2, so that device 1 and device 2 perform model training based on the AI ​​collaboration data #12.

[0058] AI collaborative data is typically large in size. To reduce the communication overhead incurred by each distributed node uploading and downloading AI collaborative data during federated learning, the AI ​​collaborative data can be quantized and compressed before transmission. Quantizing and compressing AI collaborative data can be understood as representing the AI ​​collaborative data to be transmitted (including uploaded or downloaded) using multiple bits; the number of bits used to represent the AI ​​collaborative data can be understood as the quantization level corresponding to the AI ​​collaborative data, or the quantization resolution.

[0059] It is understandable that the fewer bits used to represent AI collaborative data, the lower the quantization resolution of the AI ​​collaborative data, and the greater the error introduced during transmission (which can be understood as quantization error), which may increase the time required for model iterative convergence during federated learning. Conversely, the more bits used to represent AI collaborative data, the higher the quantization resolution of the AI ​​collaborative data, the larger the amount of data transmitted during transmission, and the longer the transmission time, which may increase the time required for model training during federated learning. It can be seen that quantization resolution can, to a certain extent, affect the training time of the federated learning model.

[0060] Typically, the distributed nodes of federated learning quantize AI collaborative data based on a fixed (or understood as predetermined) quantization resolution. However, during the federated learning process, the resources available to the distributed nodes for federated learning (including but not limited to communication resources or computing resources, etc.) may change. In this case, if the AI ​​collaborative data is always quantized at a fixed quantization resolution, the quantization resolution may not match the current distributed nodes, which may increase the training time of the federated learning model and reduce the training efficiency of federated learning.

[0061] In order to improve the training efficiency of collaborative learning, the present application provides a data processing method and a communication device. The data processing method and the communication device provided by the embodiment of the present application are described in detail below with reference to the accompanying drawings. Among them, the collaborative learning mentioned in the present application can be understood as an AI model training method in which multiple devices collaborate (or are understood as collaborative or joint) to perform training. For example, the collaborative learning can be distributed learning, federated learning, edge learning, or segmented learning.

[0062] Figure 4a is a flow chart of a data processing method provided in an embodiment of the present application. As shown in Figure 4a, the data processing method includes the following steps S401 to S405. The method execution subject shown in Figure 4a is illustrated using the first device and the second device as an example. It is understood that the method execution subject shown in Figure 4a can also be a module (e.g., a chip) in the first device and a module (e.g., a chip) in the second device.

[0063] Among them, the first device and the second device are devices participating in collaborative learning. For example, the first device can be a centralized node corresponding to collaborative learning, and the second device can be a distributed node in collaborative learning (or understood as a device that performs model training). It should be noted that the first device can be any one of the devices in RAN 100 in the communication system of Figure 1 (such as a RAN node or terminal, etc.), the functional network element in CN 200, or the server corresponding to the Internet 300. The second device can also be any one of the devices in RAN 100 in the communication system of Figure 1, the functional network element in CN 200, or the server corresponding to the Internet 300; this application does not make specific restrictions on this. Among them:

[0064] S401: The second device sends a first message to the first device, wherein the first message includes one or more of communication condition information of the second device, computing resource information of the second device, or model training information of the second device.

[0065] It can be understood that the second device is the device that uploads AI collaborative data during the collaborative learning process, and the first device is the device that determines the quantization resolution of the second device during the collaborative learning process. Taking the collaborative learning as an example, the first device can be the service node (server) of the federated learning, and the second device can be one of the multiple client nodes (client) of the federated learning.

[0066] That is to say, before the first device updates (or is understood to determine) the quantization resolution of the second device, the first device receives a first message from the second device, and the first message is used to assist the first device in updating the quantization resolution of the second device. Specifically, the second device can indicate the communication environment in which the second device is located (or called corresponding) through the communication condition information in the first message, indicate the computing resources that the second device can use for collaborative learning through the computing information in the first message, and indicate the relevant information of the second device during the model training process (such as the size of AI collaborative data or quantization resolution related data, etc.) through the model training information in the first message. It should be noted that this application does not specifically limit the way in which the second device obtains communication condition information, computing resource information and model training information.

[0067] In one possible implementation, the communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal receiving power (RSRP), reference signal receiving quality (RSRQ), signal to interference plus noise ratio (SINR), bit error rate, or throughput. The computing resource information includes one or more of the size of computing resources, the size of computing power, or computing delay. The model training information includes one or more of the test set loss value, the training set loss value, the gradient norm size, the gradient size, and the model size.

[0068] In a possible embodiment, the first message sent by the second device may include, in addition to one or more of the communication condition information of the second device, the computing resource information of the second device, or the model training information of the second device, one or more of the following information: a measurement identifier, used to identify the reporting of the first message and to associate with the subsequent second message; a second device identifier, used to identify the device that sends the first message, or understood as the device whose quantization resolution is to be updated; a first device identifier, used to identify the device that receives the first message, or understood as the device for determining the quantization resolution; a model identifier, used to identify the AI ​​model corresponding to the first message; an identifier of the training wheel corresponding to the model, used to indicate the quantization resolution used when the second device feeds back which training wheel's AI collaboration data the first message is used to update.

[0069] S402. The first device determines a quantization resolution according to one or more of the communication condition information, the computing resource information, or the model training information.

[0070] That is, after receiving the first message, the first device determines the quantization resolution based on the information included in the first message. In one possible implementation, the first device determines the quantization resolution based on one or more of communication condition information, computing resource information, or model training information, with the goal of minimizing the training time of the model.

[0071] It can be understood that in each round of training of the collaborative learning model, the first device will wait for multiple distributed nodes (including the second device) of collaborative learning to feedback AI collaborative data before executing subsequent steps (such as aggregating AI collaborative data, etc.). It can be seen that the training time of the model is affected by the delay of the distributed nodes of collaborative learning to feedback AI collaborative data. In this case, in order to minimize the training time of the model, the first device can determine a larger quantization resolution (that is, the number of bits corresponding to the quantization resolution is large) for distributed nodes with large computing power, small delay or large gradient norm to improve the accuracy of the AI ​​collaborative data fed back by the distributed node, and determine a smaller quantization resolution (that is, the number of bits corresponding to the quantization resolution is small) for distributed nodes with small computing power, large delay or small gradient norm to reduce the communication delay between the first device and the distributed node, thereby reducing the training delay.

[0072] S403: The first device sends a second message to the second device, where the second message includes indication information indicating a quantization resolution.

[0073] That is, after determining the quantization resolution of the second device, the first device indicates the quantization resolution to the second device through a second message. The indication information may indicate the quantization resolution by directly indicating the quantization resolution (i.e., the number of bits used for quantization), for example, the second message includes 4 bits or 16 bits, etc.; or it may indicate a quantization index corresponding to the quantization resolution, for example, a quantization index of 0 included in the second message indicates quantization based on 4 bits, and a quantization index of 1 included in the second message indicates quantization based on 16 bits.

[0074] In a possible implementation, in addition to the indication information indicating the quantization resolution, the second message may also include one or more of the following information: a measurement identifier, corresponding to the measurement identifier contained in the first message, used to identify the issuance of the second message; a second device identifier, used to identify the device receiving the second message, or understood as the device applying the quantization resolution; a first device identifier, used to identify the device sending the second message, or understood as the device determining the quantization resolution; a model identifier, used to identify the AI ​​model corresponding to the quantization resolution; an identifier of the training wheel corresponding to the model, used to indicate which training wheel's AI collaboration data the second device feeds back is updated and used for the quantization resolution.

[0075] S404: The second device quantizes the first AI collaboration data based on the quantization resolution to obtain second AI collaboration data.

[0076] In the training round corresponding to the quantization resolution, the second device trains the model to obtain the first AI collaboration data (i.e., the AI ​​collaboration data before quantization). Further, the second device quantizes the first AI collaboration data based on the quantization resolution to obtain the second AI collaboration data (i.e., the AI ​​collaboration data after quantization).

[0077] S405: The second device sends second AI collaboration data to the first device.

[0078] It can be understood that after the second device obtains the second AI collaboration data, the second device sends a fourth message to the first device, where the fourth message includes the second AI collaboration data. In one possible implementation, in addition to the second AI collaboration data, the fourth message may also include one or more of the following information: a second device identifier, used to identify the device sending the fourth message; a first device identifier, used to identify the device receiving the fourth message; a model identifier, used to identify the AI ​​model corresponding to the second AI collaboration data; and an identifier of the training wheel corresponding to the model, used to indicate which training wheel the second AI collaboration data belongs to.

[0079] To sum up, through the method shown in Figure 4a, the quantization resolution of the second device can be dynamically adjusted in combination with the communication conditions, computing resources or model training information of the current second device, which is conducive to improving the adaptability of the quantization resolution to the current second device. By quantizing and transmitting AI collaborative data with a quantization resolution adapted to the current second device, it is conducive to improving the training efficiency of collaborative learning.

[0080] The following two cases are further divided into the following two cases based on the triggering method of triggering the second device to send the first message to the first device to explain the collaborative learning process in detail. It should be noted that in the following two cases, only the devices participating in the collaborative learning include the first device, the second device, and the third device as an example. The devices participating in the collaborative learning may also include other devices (not shown in the figure), and this application does not make specific limitations on this.

[0081] Case 1: The second device is triggered to send the first message to the first device through passive triggering.

[0082] That is, in scenario 1, the second device receives a third message from another device (other than the second device, including the first device), and the third message is used to trigger the second device to send the aforementioned first message to the first device. In this case, the first message can be understood as a response message corresponding to the third message. For ease of understanding, the following explanation is based on the example of the third message being sent by the first device to the second device, and should not be regarded as a specific limitation on the device that sends the third message.

[0083] Please refer to FIG4b, which is a schematic diagram of a collaborative learning process provided by this application. As shown in FIG4b, the collaborative learning includes the following steps S411 to S419. Among them:

[0084] S411. The first device sends a third message to the second device, where the third message is used to request the second device to assist in updating the quantization resolution.

[0085] It can be understood that when the first device determines that it needs to update the quantization resolution of the second device, the first device sends a third message to the second device, where the third message is used to request the second device to assist in updating the quantization resolution. It should be noted that this application does not specifically limit the specific name of the third message. For example, the third message can be a request message for assistance in updating the quantization resolution.

[0086] Exemplarily, devices 1 to 5 are devices that perform federated learning tasks. Device 1 is the server corresponding to the federated learning task, and devices 2 to 5 are the clients that perform the federated learning task. During the execution of the federated learning task, each of devices 2 to 5 will feedback AI collaboration data to device 1. During a certain round (or training round (epoch)) of federated learning training, the delay for device 1 to wait for device 2 to feedback AI collaboration data #2 is delay 1, the delay for device 1 to wait for device 3 to feedback AI collaboration data #3 is delay 2, the delay for device 1 to wait for device 4 to feedback AI collaboration data #4 is delay 3, and the delay for device 1 to wait for device 5 to feedback AI collaboration data #5 is delay 4. Delay 1 is the maximum value among delays 1 to 4, and the absolute value of the difference between the average delay of delays 1 to 4 and delay 1 is greater than a first threshold; the first threshold is a preset value greater than 0, and its specific value can be adjusted according to the specific application scenario. In this case, to reduce the time Device 1 spends waiting for all clients to feedback AI collaboration data, Device 1 can adjust Device 2's quantization resolution to reduce the latency. This can be considered as Device 1 determining the need to update Device 2's quantization resolution (i.e., identifying Device 2 as the second device). Furthermore, Device 1 sends a third message to Device 2, requesting Device 2 to assist Device 1 in updating its quantization resolution, i.e., requesting Device 2 to send the first message to Device 1.

[0087] In a possible implementation manner, the third message includes one or more of the following information:

[0088] ① Measurement ID, also known as measurement ID, is used to indicate the request for assistance in updating the quantization resolution.

[0089] ② The identifier of the second device, used to indicate the device whose quantization resolution is to be updated, or used to indicate the device that receives the third message, or used to indicate the device that sends the first message.

[0090] ③ The identifier of the first device, used to indicate the device that determines the quantization resolution, or used to indicate the device that sends the third message, or used to indicate the device that receives the first message.

[0091] ④ Model identification, used to indicate that the second device needs to update the AI ​​model (or AI collaboration model) of the quantization resolution. The model identification can be the identification of the AI ​​model, or associated information associated with the AI ​​model (such as the service identification corresponding to the AI ​​model).

[0092] ⑤ Indication information indicating the reason for updating the quantization resolution, used to indicate the reason for this update of the quantization resolution. Exemplarily, the third message includes field A, and the value of field A is used to indicate the reason for this update of the quantization resolution. When the value of field A is 1, the third message indicates that the reason for updating the quantization resolution is: the latency of the second device (i.e., the latency of the first device waiting for the second device to feedback the AI ​​collaborative data) is large, or it is understood that the efficiency of the second device in feedbacking the AI ​​collaborative data needs to be improved and the quantization resolution needs to be updated. When the value of field A is 0, the third message indicates that the reason for updating the quantization resolution is: the error of the AI ​​collaborative data fed back by the second device is large, or it is understood that the accuracy of the AI ​​collaborative data fed back by the second device needs to be improved and the quantization resolution needs to be updated.

[0093] In one possible embodiment, the indication information indicating the reason for updating the quantization resolution includes the time difference between the first delay and the average delay, or the ratio between the first delay and the average delay. The first delay is the delay of the first device waiting for the second device to feedback the AI ​​collaborative data, and the average delay is the average value of the delay of the first device waiting for multiple distributed nodes to feedback the AI ​​collaborative data, and the multiple distributed nodes include the second device. It can be understood that when the reason for updating the quantization resolution is that the delay of the second device is large (or it is understood that the quantization resolution needs to be updated to improve the efficiency of the second device in feedbacking AI collaborative data), the indication information included in the third message can be the time difference between the first delay and the average delay, or the ratio between the first delay and the average delay.

[0094] ⑥ The identifier of the training wheel corresponding to the model is used to indicate which training wheel’s AI collaboration data the second device feeds back based on the first message, and the quantization resolution used. For example, the identifier of the Nth training wheel of the model included in the third message indicates that the first device updates the quantization resolution used when the second device feeds back the AI ​​collaboration data of the Nth training wheel based on the first message, that is, when the second device subsequently feeds back the AI ​​collaboration data of the Nth training wheel, it can use the quantization resolution updated based on the first message for quantization. It should be noted that in this application, training wheels may also be referred to as wheels, as stated throughout the text.

[0095] ⑦ An identifier of the information requested for feedback, used to indicate that the first device expects (or understands as a request) information from the second device for feedback. The information requested for feedback includes one or more of communication condition information, computing resource information, or model training information. The second device may subsequently generate a first message based on the identifier of the information requested for feedback, i.e., the first message is generated based on the identifier of the information requested for feedback.

[0096] It should be noted that the information included in the first message may be part or all of the information requested for feedback by the third message. For example, the first device requests the second device to feedback communication condition information, computing resource information and model training information, then the identifier of the information requested for feedback in the third message sent by the first device includes: an identifier of the communication condition information (such as an identifier of the transmission delay or an identifier of the RSRP, etc.), an identifier of the computing resource information and an identifier of the model training information (such as an identifier of the test set loss value or an identifier of the gradient norm size). In this case, the second device can feedback only part (that is, feedback one or two of the communication condition information, computing resource information or model training information) according to the third message, or it can feedback all (that is, feedback communication condition information, computing resource information and model training information).

[0097] ⑧ Configuration information for transmitting the first message, used to instruct the second device how to transmit the first message. In one possible implementation, the configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a trigger condition for transmitting the first message, or a number of times the first message is transmitted. Wherein:

[0098] The transmission period for transmitting the first message may be determined based on a time interval value / index or based on a training round. Taking the transmission period determined based on a training round as an example, if the configuration information indicates that the transmission period for the first message is three training rounds, the configuration information instructs the second device to transmit the first message once every three training rounds.

[0099] The triggering condition for transmitting the first message may include one or more of the following conditions: 1. A triggering condition for triggering instant transmission of the first message (i.e., the second device transmits the first message once if the triggering condition is met). For example, the triggering condition is that the gradient norm of the second device is greater than or equal to the gradient norm threshold, the communication bandwidth is less than or equal to the bandwidth threshold, the channel quality (e.g., RSRP, RSRQ, SINR, bit error rate, throughput, etc.) is less than or equal to the channel quality threshold, the computing power is less than or equal to the computing power threshold, etc.; 2. A triggering condition for triggering periodic transmission of the first message (i.e., the second device transmits the first message according to the transmission period, period if the triggering condition is met). The trigger condition for ending the periodic transmission of the first message (that is, when the second message periodically transmits the first message, if the second device meets the trigger condition, the second device stops periodically transmitting the first message), for example, the trigger condition is that the gradient norm of the second device is less than the gradient norm threshold, the communication bandwidth is less than or equal to the bandwidth threshold, the channel quality is less than or equal to the channel quality threshold, the computing power is greater than the computing power threshold, etc.; 3. The trigger condition for ending the periodic transmission of the first message (that is, when the second message periodically transmits the first message, if the second device meets the trigger condition, the second device stops periodically transmitting the first message), for example, the trigger condition is that the gradient norm of the second device is less than the gradient norm threshold, the communication bandwidth is greater than the bandwidth threshold, the channel quality is greater than the channel quality threshold, the computing power is greater than the computing power threshold, etc.

[0100] The number of times the first message is transmitted is used to indicate the total number of times the second device needs to transmit the first message after receiving the configuration information for transmitting the first message.

[0101] S412. The second device sends the first message to the first device according to the third message.

[0102] It can be understood that the first message is understood as a response message to the third message. For example, the measurement identifier in the first message corresponds to (or is understood to be the same as) the measurement identifier included in the third message, or the model identifier in the first message is the same as the model identifier included in the third message, or the training wheel identifier corresponding to the model in the first message is the same as the training wheel identifier corresponding to the model included in the third message.

[0103] It should be noted that the present application does not specifically limit the specific name of the first message. For example, the first message can be a response message for assisting in updating the quantization resolution. For a detailed description of the first message, please refer to the detailed description of the first message in S401 above, which will not be repeated here.

[0104] S413. The first device determines a quantization resolution according to one or more of the communication condition information, the computing resource information, or the model training information.

[0105] S414. The first device sends a second message to the second device, where the second message includes indication information indicating a quantization resolution.

[0106] S415. The second device quantizes the first AI collaboration data based on the quantization resolution to obtain second AI collaboration data.

[0107] S416. The second device sends second AI collaboration data to the first device.

[0108] The specific implementation process of S413 to S416 can be found in the description of the specific implementation process of S402 to S405 above, which will not be repeated here.

[0109] S417: The third device sends third AI collaboration data to the first device.

[0110] It can be understood that the third device trains the model to obtain AI collaboration data of the third device; further, the third device quantizes the AI ​​collaboration data of the third device based on its corresponding quantization resolution to obtain third AI collaboration data, and sends the third AI collaboration data to the first device.

[0111] It should be noted that the specific content of the AI ​​collaboration data of the third device can be found in the specific description of the AI ​​collaboration data described above. For example, the AI ​​collaboration data of the third device includes one or more of the following data: gradient data, AI model, AI sub-model, AI model output, AI model intermediate value, etc. The quantization resolution of the third device can also be updated by referring to the data processing method provided in Figure 4a above, and this application does not specifically limit this.

[0112] S418. The first device aggregates the second AI collaboration data and the third AI collaboration data to obtain aggregated AI collaboration data.

[0113] That is, after the first device receives the AI ​​collaboration data fed back from all (or part) of the distributed nodes (such as the second device and the third device) participating in the collaborative learning, it aggregates the AI ​​collaboration data to obtain aggregated AI collaboration data.

[0114] S419. The first device sends the aggregated AI collaboration data to the second device and the third device respectively.

[0115] That is to say, after the first device obtains the aggregated AI collaborative data, the first device sends the aggregated AI collaborative data to the distributed nodes participating in collaborative learning, so that the distributed nodes participating in collaborative learning (including the second device and the third device) respectively perform model training based on the aggregated AI collaborative data.

[0116] It should be noted that when the first device sends aggregated AI collaborative data to the distributed nodes participating in collaborative learning, it can also indicate the identifier of the distributed node (for example, the identifier of the second device or the identifier of the third device) to indicate the device receiving the aggregated AI collaborative data; the first device identifier is used to identify the device sending the aggregated AI collaborative data; the model identifier is used to indicate the AI ​​model corresponding to the aggregated AI collaborative data; the identifier of the training wheel corresponding to the model is used to indicate which training wheel of the model the aggregated AI collaborative data corresponds to. It should also be noted that the aggregated AI collaborative data sent by the first device to the distributed nodes participating in collaborative learning can be unquantized data (that is, it can be understood as aggregated data obtained by aggregating the second AI collaborative data and the third AI collaborative data), or quantized data (that is, it can be understood as aggregated data obtained by aggregating the second AI collaborative data and the third AI collaborative data, and then subjected to an aggregated quantization resolution quantization operation). This application does not make specific restrictions on this. Among them, the aggregated quantization resolution can be understood as the quantization resolution of the quantized aggregated AI collaborative data, and the aggregated quantization resolution can also be determined based on one or more of the communication condition information of the first device, the communication condition information of the second device, or the computing resource information of the second device. This application does not make specific restrictions.

[0117] Case 2: The second device is triggered to send the first message to the first device through active triggering.

[0118] Please refer to Figure 4c, which is a schematic diagram of another collaborative learning process provided by this application. As shown in Figure 4c, the collaborative learning includes the following steps S421 to S428. Among them:

[0119] S421: The second device sends a first message to the first device, where the first message is used to request the first device to update the quantization resolution of the second device.

[0120] That is, when the second device determines that it needs to update the quantization resolution, the second device proactively sends a first message to the first device based on the need to update the quantization resolution. It should be noted that this application does not specifically limit the specific name of the first message. For example, the first message can be a request message for updating the quantization resolution. For a detailed description of the first message, please refer to the detailed description of the first message in S401 above and will not be repeated here.

[0121] For example, device 1 and device 2 are devices that perform federated learning tasks. Device 1 is the server that performs the federated learning task, and device 2 is the client that performs the federated learning task. In the process of performing the federated learning task, device 2 will feedback AI collaboration data to device 1. During a round of training for federated learning, if device 2 detects that one or more of the following conditions are met: the gradient norm of the AI ​​model is greater than or equal to the gradient norm threshold, the communication bandwidth of the second device is less than or equal to the bandwidth threshold, the channel quality of the second device is less than or equal to the channel quality threshold, the computing power of the second device is less than or equal to the computing power threshold, etc., it can be regarded as a need to update the quantization resolution of device 2, and device 2 actively sends a first message to the first device.

[0122] S422. The first device determines a quantization resolution based on one or more of the communication condition information, computing resource information, or model training information.

[0123] S423: The first device sends a second message to the second device, where the second message includes indication information indicating a quantization resolution.

[0124] S424. The second device quantizes the first AI collaboration data based on the quantization resolution to obtain second AI collaboration data.

[0125] S425. The second device sends second AI collaboration data to the first device.

[0126] S426. The third device sends third AI collaboration data to the first device.

[0127] S427. The first device aggregates the second AI collaboration data and the third AI collaboration data to obtain aggregated AI collaboration data.

[0128] S428. The first device sends the aggregated AI collaboration data to the second device and the third device respectively.

[0129] The specific implementation process of S422 to S428 can be found in the description of the specific implementation process of S413 to S419 above, which will not be repeated here.

[0130] It is understandable that in order to implement the functions in the above embodiments, the device (including the aforementioned first device or second device) includes a hardware structure and / or software module corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and method steps of each example described in the embodiments disclosed in this application, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software transceiver components driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0131] Figures 5 and 6 are schematic diagrams of the structures of possible communication devices provided in the embodiments of the present application. These communication devices can be used to implement the functions of the devices in the above-mentioned method embodiments, and thus can also achieve the beneficial effects possessed by the above-mentioned method embodiments. In the embodiments of the present application, the communication device can be the first device in Figures 4a to 4c, or a module (such as a chip) applied to the first device, or the communication device can be the second device in Figures 4a to 4c, or a module (such as a chip) applied to the second device.

[0132] As shown in Figure 5, the communication device 500 includes a processing unit 510 and a transceiver unit 520. The communication device 500 is used to implement the functions of the first device or the second device in the method embodiments shown in Figures 4a to 4c above.

[0133] When the communication device 500 is used to implement the function of the first device in the method embodiment shown in Figures 4a to 4c: the transceiver unit 520 is used to send a first message to the first device, where the first message includes one or more of the communication condition information of the second device, the computing resource information of the second device, or the model training information of the second device; the transceiver unit 520 is also used to receive a second message from the first device, where the second message includes indication information, where the indication information indicates a quantization resolution, and the quantization resolution is related to one or more of the communication condition information, the computing resource information, or the model training information; the processing unit 510 is used to quantize the first artificial intelligence (AI) collaborative data based on the quantization resolution to obtain second AI collaborative data; the transceiver unit 520 is also used to send the second AI collaborative data to the first device.

[0134] In one possible implementation, the communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal received power RSRP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, bit error rate or throughput; the computing resource information includes one or more of the size of computing resources, the size of computing power or computing delay; the model training information includes one or more of the test set loss value, the training set loss value, the gradient norm size, the gradient size, and the model size.

[0135] In a possible implementation, the transceiver unit 520 is further configured to receive a third message from the first device, where the third message is used to request assistance in updating the quantization resolution; and the first message is a response message corresponding to the third message.

[0136] In one possible implementation, the third message includes one or more of the following information: a measurement identifier, an identifier of the second device, an identifier of the first device, an identifier of the model, indication information indicating the reason for updating the quantization resolution, an identifier of the training wheel corresponding to the model, an identifier of the information requested for feedback, or configuration information for transmitting the first message; wherein the information requested for feedback includes one or more of communication condition information, computing resource information, or model training information; and the first message is generated based on the identifier of the information requested for feedback.

[0137] In one possible implementation, the indication information indicating the reason for updating the quantization resolution includes a time difference between a first delay and an average delay, or a ratio between the first delay and the average delay; wherein the first delay is the delay of the first device waiting for the second device to feedback the AI ​​collaboration data, and the average delay is the average value of the delays of the first device waiting for multiple distributed nodes to feedback the AI ​​collaboration data, where the multiple distributed nodes include the second device.

[0138] In a possible implementation, the configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a triggering condition for transmitting the first message, or the number of times the first message is transmitted.

[0139] In a possible implementation, the first message further includes one or more of the following information: a measurement identifier, a second device identifier, a first device identifier, an identifier of the model, and an identifier of a training wheel corresponding to the model.

[0140] For a more detailed description of the transceiver unit 520 and the processing unit 510 , reference may be made to the related description of the second device in the method embodiment shown in FIG. 4 a to FIG. 4 c .

[0141] As shown in Figure 5, the communication device 500 includes a processing unit 510 and a transceiver unit 520. The communication device 500 is used to implement the functions of the first device in the method embodiments shown in Figures 4a to 4c above.

[0142] When the communication device 500 is used to implement the function of the first device in the method embodiment shown in Figures 4a to 4c: the transceiver unit 520 is used to receive a first message from the second device, and the first message includes one or more of the communication condition information of the second device, the computing resource information of the second device, or the model training information of the second device; the processing unit 510 is used to determine the quantization resolution based on one or more of the communication condition information, the computing resource information, or the model training information; the transceiver unit 520 is also used to send a second message to the second device, and the second message includes indication information, and the indication information indicates the quantization resolution, and the quantization resolution is related to one or more of the communication condition information, the computing resource information, or the model training information; the transceiver unit 520 is also used to receive second artificial intelligence (AI) collaborative data from the second device, and the second AI collaborative data is obtained by quantizing the first AI collaborative data based on the quantization resolution.

[0143] In one possible implementation, the communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal received power RSRP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, bit error rate or throughput; the computing resource information includes one or more of the size of computing resources, the size of computing power or computing delay; the model training information includes one or more of the test set loss value, the training set loss value, the gradient norm size, the gradient size, and the model size.

[0144] In a possible implementation, the transceiver unit 520 is further configured to send a third message to the second device, where the third message is used to request assistance in updating the quantization resolution; and the first message is a response message corresponding to the third message.

[0145] In one possible implementation, the third message includes one or more of the following information: a measurement identifier, an identifier of the second device, an identifier of the first device, a model identifier, indication information indicating the reason for updating the quantization resolution, an identifier of the training wheel corresponding to the model, an identifier of the information requested for feedback, and / or configuration information for transmitting the first message; wherein the information requested for feedback includes one or more of communication condition information, computing resource information, or model training information; and the first message is generated based on the identifier of the information requested for feedback.

[0146] In one possible implementation, the indication information indicating the reason for updating the quantization resolution includes a time difference between a first delay and an average delay, or a ratio between the first delay and the average delay; wherein the first delay is the delay of the first device waiting for the second device to feedback the AI ​​collaboration data, and the average delay is the average value of the delays of the first device waiting for multiple distributed nodes to feedback the AI ​​collaboration data, where the multiple distributed nodes include the second device.

[0147] In a possible implementation, the configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a triggering condition for transmitting the first message, or the number of times the first message is transmitted.

[0148] In a possible implementation, the first message further includes one or more of the following information: a measurement identifier, a second device identifier, a first device identifier, an identifier of the model, and an identifier of a training wheel corresponding to the model.

[0149] For a more detailed description of the transceiver unit 520 and the processing unit 510 , reference may be made to the related description of the first device in the method embodiment shown in FIG. 4 a to FIG. 4 c .

[0150] As shown in Figure 6, communication device 600 includes a processor 610 and an interface circuit 620. Processor 610 and interface circuit 620 are coupled to each other. It is understood that interface circuit 620 can be a transceiver or an input / output interface. Optionally, communication device 600 may also include a memory 630 for storing instructions executed by processor 610, input data required by processor 610 to execute instructions, or data generated after processor 610 executes instructions.

[0151] When the communication device 600 is used to implement the method shown in FIG. 4 a to FIG. 4 c , the processor 610 is used to implement the functions of the processing unit 510 , and the interface circuit 620 is used to implement the functions of the transceiver unit 520 .

[0152] When the communication device is a chip implemented in the first device, the chip implements the functions of the first device in the above method embodiment. When the chip receives information from the second device, it can be understood that the information is first received by other modules in the first device (such as a radio frequency module or antenna) and then sent to the chip by these modules. When the chip sends information to the second device, it can be understood that the information is first sent to other modules in the first device (such as a radio frequency module or antenna) and then sent to the second device by these modules.

[0153] When the communication device is a chip used in a second device, the chip implements the functions of the second device in the above method embodiment. When the chip receives information from the first device, it can be understood that the information is first received by other modules in the second device (such as a radio frequency module or antenna) and then sent to the chip by these modules. When the chip sends information to the first device, it can be understood that the information is first sent to other modules in the second device (such as a radio frequency module or antenna) and then sent to the first device by these modules.

[0154] In this application, when entity A sends information to entity B, it can be done directly from A to B or indirectly through another entity. Similarly, when entity B receives information from entity A, it can be done directly from entity B or indirectly through another entity. Entities A and B herein can be RAN nodes or terminals, or modules within a RAN node or terminal. The sending and receiving of information can be information exchange between a RAN node and a terminal, for example, between a base station and a terminal; the sending and receiving of information can also be information exchange between two RAN nodes, for example, between a CU and a DU; the sending and receiving of information can also be information exchange between different modules within a device, for example, between a terminal chip and other modules in the terminal, or between a base station chip and other modules within the base station.

[0155] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0156] The method steps in the embodiments of the present application can be implemented in hardware or in software instructions that can be executed by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disk, mobile hard disk, CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. The storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a base station or a terminal. The processor and storage medium can also exist in a base station or a terminal as discrete components.

[0157] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are performed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable device. The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video disk; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

[0158] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0159] In this application, "at least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In the text description of this application, the character " / " generally indicates that the previous and next associated objects are in an "or" relationship; in the formula of this application, the character " / " indicates that the previous and next associated objects are in a "division" relationship. "Including at least one of A, B and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B and C.

[0160] It is understood that the various numbers used in the embodiments of this application are merely for ease of description and are not intended to limit the scope of the embodiments of this application. The order of the sequence numbers of the above-mentioned processes does not necessarily imply a specific order of execution; the order of execution of the processes should be determined by their functions and inherent logic.

Claims

1. A data processing method, characterized in that: The method comprises: Sending a first message to a first device, where the first message includes one or more of communication condition information of a second device, computing resource information of the second device, or model training information of the second device; receiving a second message from the first device, the second message including indication information, the indication information indicating a quantization resolution, the quantization resolution being related to one or more of the communication condition information, the computing resource information, or the model training information; quantizing the first artificial intelligence AI collaboration data based on the quantization resolution to obtain second AI collaboration data; Send the second AI collaboration data to the first device.

2. The method according to claim 1, characterized in that: The communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal received power RSRP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, bit error rate or throughput; The computing resource information includes one or more of the size of the computing resource, the size of the computing capability, or the computing delay; The model training information includes one or more of a test set loss value, a training set loss value, a gradient norm size, a gradient size, and a model size.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: receiving a third message from the first device, wherein the third message is used to request assistance in updating a quantization resolution; The first message is a response message corresponding to the third message.

4. The method according to claim 3, characterized in that: The third message includes one or more of the following information: a measurement identifier, an identifier of the second device, an identifier of the first device, an identifier of a model, indication information indicating a reason for updating a quantization resolution, an identifier of a training wheel corresponding to the model, an identifier of information requesting feedback, or configuration information for transmitting the first message; The information requested for feedback includes one or more of the communication condition information, the computing resource information or the model training information; and the first message is generated according to the identifier of the information requested for feedback.

5. The method according to claim 4, characterized in that: The indication information indicating the reason for updating the quantization resolution includes a time difference between the first delay and the average delay, or a ratio between the first delay and the average delay; The first delay is the delay of the first device waiting for the second device to feedback the AI ​​collaboration data, and the average delay is the average value of the delay of the first device waiting for multiple distributed nodes to feedback the AI ​​collaboration data, and the multiple distributed nodes include the second device.

6. The method according to claim 4 or 5, characterized in that: The configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a triggering condition for transmitting the first message, or a number of times of transmitting the first message.

7. The method according to any one of claims 1 to 6, characterized in that: The first message also includes one or more of the following information: A measurement identifier, the second device identifier, the first device identifier, an identifier of a model, and an identifier of a training wheel corresponding to the model.

8. A data processing method, characterized in that: The method comprises: receiving a first message from a second device, the first message including one or more of communication condition information of the second device, computing resource information of the second device, or model training information of the second device; Determine a quantization resolution based on one or more of the communication condition information, the computing resource information, or the model training information; Sending a second message to the second device, where the second message includes indication information, where the indication information indicates a quantization resolution, where the quantization resolution is related to one or more of the communication condition information, the computing resource information, or the model training information; Second artificial intelligence (AI) collaboration data is received from the second device, where the second AI collaboration data is obtained by quantizing the first AI collaboration data based on the quantization resolution.

9. The method according to claim 8, characterized in that: The communication condition information includes one or more of communication bandwidth information, transmission delay, reference signal received power RSRP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, bit error rate or throughput; The computing resource information includes one or more of the size of the computing resource, the size of the computing capability, or the computing delay; The model training information includes one or more of a test set loss value, a training set loss value, a gradient norm size, a gradient size, and a model size.

10. The method according to claim 8 or 9, characterized in that: The method further comprises: Sending a third message to the second device, where the third message is used to request assistance in updating the quantization resolution; The first message is a response message corresponding to the third message.

11. The method according to claim 10, characterized in that: The third message includes one or more of the following information: a measurement identifier, an identifier of the second device, an identifier of the first device, a model identifier, indication information indicating a reason for updating a quantization resolution, an identifier of a training round corresponding to the model, an identifier of information requesting feedback, and / or configuration information for transmitting the first message; The information requested for feedback includes one or more of the communication condition information, the computing resource information or the model training information; and the first message is generated according to the identifier of the information requested for feedback.

12. The method according to claim 11, characterized in that: The indication information indicating the reason for updating the quantization resolution includes a time difference between a first delay and an average delay, or a ratio between the first delay and the average delay; wherein the first delay is a delay for the first device to wait for the second device to feedback the AI ​​collaboration data, and the average delay is an average value of delays for the first device to wait for multiple distributed nodes to feedback the AI ​​collaboration data, and the multiple distributed nodes include the second device.

13. The method according to claim 11 or 12, characterized in that: The configuration information for transmitting the first message includes one or more of a transmission period for transmitting the first message, a triggering condition for transmitting the first message, or a number of times of transmitting the first message.

14. The method according to any one of claims 8 to 13, characterized in that: The first message also includes one or more of the following information: A measurement identifier, the second device identifier, the first device identifier, an identifier of a model, and an identifier of a training wheel corresponding to the model.

15. A communication device, characterized in that: The method comprises a module for executing the method according to any one of claims 1 to 7, or comprises a module for executing the method according to any one of claims 8 to 14.

16. A communication device, characterized in that: It includes a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices outside the communication device and transmit them to the processor or send signals from the processor to other communication devices outside the communication device, and the processor is used to implement the method as described in any one of claims 1 to 7 through a logic circuit or by executing code instructions, or the processor is used to implement the method as described in any one of claims 8 to 14 through a logic circuit or by executing code instructions.

17. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction. When the computer program or instruction is executed by the communication device, the communication device implements the method as described in any one of claims 1 to 7, or implements the method as described in any one of claims 8 to 14.

18. A computer program product, characterized in that The computer program product comprises a computer program or instructions, and when the computer program or instructions are executed by a communication device, the communication device implements the method according to any one of claims 1 to 7, or implements the method according to any one of claims 8 to 14.

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