Methods and apparatuses for vertical federated learning for network analytics services

Abstract

A network exposure function (NEF) may be configured to receive a service request for Vertical Federated Learning (VFL) initialization. The NEF may determine a list of wireless transmit / receive unit (WTRUs) to use as samples for VFL training. The NEF may also send a service response for VFL initialization. The service response for VFL initialization may indicate the list of WTRUs to use as samples for VFL training. Further, the NEF may receive a confirmation for VFL initialization, and perform VFL operations.

Description

METHODS AND APPARATUSES FOR VERTICAL FEDERATED LEARNING FOR NETWORK ANALYTICS SERVICESCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application number 63 / 574,669, filed April 4, 2024, the contents of which are incorporated herein by reference in their entirety.BACKGROUND

[0002] Network Data Analytics features in cellular networks may provide statistics and / or predictions based on specific requests from the entities consuming this information. Some examples of the types of information that the features may be capable of providing include statistics and predictions on gNB status information, gNB resource usage, and communication / mobility performance in an Area of Interest. The target of such analytics may comprise of a single wireless transmit / receive unit (WTRU), a group of WTRUs or any WTRU in an Area of Interest. Furthermore, Network data analytics may be provided to characterize Network Function load, and / or Network Slice load, as well as data analytics that can provide predictions and statistics regarding WTRU mobility, expected WTRU behavior and even observed service experience at multiple levels, including Network Slice, service experience for a particular application or service experience for a particular application over a particular access type (e.g., Radio Access Technology (RAT) type or frequency).SUMMARY

[0003] A network exposure function (NEF) may be configured to receive (e.g., from an application function (AF) a service request for Vertical Federated Learning (VFL) initialization and / or alignment. The request may include initialization and / or alignment information, for example, including sample selection criteria, and / or a request for a machine learning model. The NEF may perform an alignment procedure with a network function (e.g., based on the sample selection criteria). The NEF may determine a list of wireless transmit / receive unit (WTRUs) as sample. The NEF may also send the service response for VFL initialization or alignment. The response may include parameters for VFL alignment. Further, the NEF may receive a confirmation for VFL initialization, and perform a VFL operation (e.g., VFL training, sharing of VFL results).

[0004] The service request for VFL initialization may be based on an analytic ID, a requested machine learning (ML) model, a list of requested (e.g., available) features, a requested timeperiod, and selection criteria for sample WTRUs. The NEF may evaluate one or more WTRUs and determine whether they meet the sample selection criteria. The NEF may exclude one or more WTRUs if it determines that they do not meet the sample selection criteria. The parameters for VFL alignment may indicate an analytic service and / or an associated ML model.

[0005] The NEF may also discover and select a network function, for example, a federated learning (FL) server network data analytics function (NWDAF). In an example, the service response for VFL initialization is based on an analytic ID, an available ML model, a list of available features, and a recommended list of WTRUs as sample. In one embodiment, the confirmation for VFL initialization is based on a list of available features, and the list of WTRUs as sample. In another example, the list of WTRU satisfies the selection criteria.

[0006] The NEF may receive an initiation message for VFL training. For example, the initiation message may comprise a list of available features, and / or a list of WTRUs selected for VFL training. The NEF may perform VFL operations based on the initiation message, for example, using the list of features and / or list of WTRUs included in the initiation message. VFL operations may include VFL training and / or sharing of locally trained VFL results.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1A is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented.

[0008] FIG. 1 B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0009] FIG. 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0010] FIG. 1 D is a system diagram illustrating a further example RAN and a further example CN that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0011] FIG. 2 depicts a flowchart illustrating an exemplary procedure of Application Function (AF) initiated Vertical Federated Learning (VFL) operation for Network (NW) analytics.DETAILED DESCRIPTION

[0012] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0013] As shown in FIG. 1A, the communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a CN 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a “station” and / or a “STA”, may be configured to transmit and / or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a WTRU. Further, any description herein that is described with reference to a UE may be equally applicable to a WTRU (or vice versa). For example, a WTRU may be configured to perform any of the processes or procedures described herein as being performed by a UE (or vice versa).

[0014] The communications systems 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate accessto one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0015] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e. , one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in desired spatial directions.

[0016] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0017] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and / or EvolvedHSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and / or High-Speed UL Packet Access (HSUPA).

[0018] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE- Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0019] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0020] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., an eNB and a gNB).

[0021] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0022] The base station 114b in FIG. 1 A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellularbased RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1 A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106 / 115.

[0023] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 / 115 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may be utilizing a NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0024] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or the other networks 112. The PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and / or the internet protocol (IP) in the TCP / IP internet protocol suite. The networks 112 may include wired and / or wireless communications networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0025] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0026] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset136, and / or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0027] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0028] The transmit / receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0029] Although the transmit / receive element 122 is depicted in FIG. 1B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0030] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 122 and to demodulate the signals that are received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11 , for example.

[0031] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g.,a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and / or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0032] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0033] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and / or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable location-determination method while remaining consistent with an embodiment.

[0034] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and / or Augmented Reality (VR / AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor,a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0035] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and / or simultaneous. The full duplex radio may include an interference management unit 139 to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0036] FIG. 1C is a system diagram illustrating the RAN 104 and the ON 106 according to an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0037] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a.

[0038] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, and the like. As shown in FIG. 1 C, the eNode-Bs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0039] The CN 106 shown in FIG. 1C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements are depicted as part of the CN 106, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0040] The MME 162 may be connected to each of the eNode-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, beareractivation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0041] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via the S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring user planes during inter-eNode B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing contexts of the WTRUs 102a, 102b, 102c, and the like.

[0042] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0043] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit- switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers.

[0044] Although the WTRU is described in FIGS. 1A-1D as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily, or permanently) wired communication interfaces with the communication network.

[0045] In representative embodiments, the other network 112 may be a WLAN.

[0046] A WLAN in Infrastructure Basic Service Set (BSS) mode may have an Access Point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have an access or an interface to a Distribution System (DS) or another type of wired / wireless network that carries traffic in to and / or out of the BSS. Traffic to STAs that originates from outside the BSS may arrive through the AP and may be delivered to the STAs. Traffic originating from STAs to destinations outside the BSS may be sent to the AP to be delivered to respective destinations. Traffic between STAs within the BSS may be sent through the AP, for example, where the source STA may send traffic to the AP and the AP may deliver the traffic to the destination STA. The traffic between STAs within a BSS may be considered and / or referred toas peer-to-peer traffic. The peer-to-peer traffic may be sent between (e.g., directly between) the source and destination STAs with a direct link setup (DLS). In certain representative embodiments, the DLS may use an 802.11e DLS or an 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and the STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The IBSS mode of communication may sometimes be referred to herein as an “ad-hoc” mode of communication.

[0047] When using the 802.11 ac infrastructure mode of operation or a similar mode of operations, the AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., 20 MHz wide bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS and may be used by the STAs to establish a connection with the AP. In certain representative embodiments, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented, for example in in 802.11 systems. For CSMA / CA, the STAs (e.g., every ST A), including the AP, may sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0048] High Throughput (HT) STAs may use a 40 MHz wide channel for communication, for example, via a combination of the primary 20 MHz channel with an adjacent or nonadjacent 20 MHz channel to form a 40 MHz wide channel.

[0049] Very High Throughput (VHT) STAs may support 20MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. The 40 MHz, and / or 80 MHz, channels may be formed by combining contiguous 20 MHz channels. A 160 MHz channel may be formed by combining 8 contiguous 20 MHz channels, or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, the data, after channel encoding, may be passed through a segment parser that may divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing, and time domain processing, may be done on each stream separately. The streams may be mapped on to the two 80 MHz channels, and the data may be transmitted by a transmitting STA. At the receiver of the receiving STA, the above described operation for the 80+80 configuration may be reversed, and the combined data may be sent to the Medium Access Control (MAC).

[0050] Sub 1 GHz modes of operation are supported by 802.11af and 802.11 ah. The channel operating bandwidths, and carriers, are reduced in 802.11af and 802.11 ah relative to those used in 802.11 n, and 802.11ac. 802.11af supports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz,and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support Meter Type Control / Machine-Type Communications, such as MTC devices in a macro coverage area. MTC devices may have certain capabilities, for example, limited capabilities including support for (e.g., only support for) certain and / or limited bandwidths. The MTC devices may include a battery with a battery life above a threshold (e.g., to maintain a very long battery life).

[0051] WLAN systems, which may support multiple channels, and channel bandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11 ah, include a channel which may be designated as the primary channel. The primary channel may have a bandwidth equal to the largest common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA, from among all STAs in operating in a BSS, which supports the smallest bandwidth operating mode. In the example of 802.11 ah, the primary channel may be 1 MHz wide for STAs (e.g., MTC type devices) that support (e.g., only support) a 1 MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) settings may depend on the status of the primary channel. If the primary channel is busy, for example, due to a STA (which supports only a 1 MHz operating mode), transmitting to the AP, the entire available frequency bands may be considered busy even though a majority of the frequency bands remains idle and may be available.

[0052] In the United States, the available frequency bands, which may be used by 802.11 ah, are from 902 MHz to 928 MHz. In Korea, the available frequency bands are from 917.5 MHz to 923.5 MHz. In Japan, the available frequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.11 ah is 6 MHz to 26 MHz depending on the country code.

[0053] FIG. 1 D is a system diagram illustrating the RAN 113 and the CN 115 according to an embodiment. As noted above, the RAN 113 may employ an NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0054] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment.The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, gNBs 180a, 108b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a, for example, may use multiple antennas to transmit wireless signals to,and / or receive wireless signals from, the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on unlicensed spectrum while the remaining component carriers may be on licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0055] The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using transmissions associated with a scalable numerology. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using subframe or transmission time intervals (TTIs) of various or scalable lengths (e.g., containing varying number of OFDM symbols and / or lasting varying lengths of absolute time).

[0056] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c without also accessing other RANs (e.g., such as eNode-Bs 160a, 160b, 160c). In the standalone configuration, WTRUs 102a, 102b, 102c may utilize one or more of gNBs 180a, 180b, 180c as a mobility anchor point. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration WTRUs 102a, 102b, 102c may communicate with / connect to gNBs 180a, 180b, 180c while also communicating with / connecting to another RAN such as eNode-Bs 160a, 160b, 160c. For example, WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In the non-standalone configuration, eNode-Bs 160a, 160b, 160c may serve as a mobility anchor for WTRUs 102a, 102b, 102c and gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for servicing WTRUs 102a, 102b, 102c.

[0057] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards User Plane Function (UPF) 184a, 184b, routing of control plane information towards Access and MobilityManagement Function (AMF) 182a, 182b and the like. As shown in FIG. 1 D, the gNBs 180a, 180b, 180c may communicate with one another over an Xn interface.

[0058] The CN 115 shown in FIG. 1D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements are depicted as part of the CN 115, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0059] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, support for network slicing (e.g., handling of different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, management of the registration area, termination of NAS signaling, mobility management, and the like. Network slicing may be used by the AMF 182a, 182b in order to customize CN support for WTRUs 102a, 102b, 102c based on the types of services being utilized WTRUs 102a, 102b, 102c. For example, different network slices may be established for different use cases such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, and / or the like. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non- 3GPP access technologies such as WiFi.

[0060] The SMF 183a, 183b may be connected to an AMF 182a, 182b in the CN 115 via an N11 interface. The SMF 183a, 183b may also be connected to a UPF 184a, 184b in the CN 115 via an N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b and configure the routing of traffic through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating WTRU IP address, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notifications, and the like. A PDU session type may be IP-based, non-IP based, Ethernet-based, and the like.

[0061] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies,supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, and the like.

[0062] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to a local Data Network (DN) 185a, 185b through the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and an N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0063] In view of Figures 1A-1 D, and the corresponding description of Figures 1A-1D, one or more, or all, of the functions described herein with regard to one or more of: WTRU 102a-d, Base Station 114a-b, eNode-B 160a-c, MME 162, SGW 164, PGW 166, gNB 180a-c, AMF 182a-ab, UPF 184a-b, SMF 183a-b, DN 185a-b, and / or any other device(s) described herein, may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0064] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and / or may performing testing using over-the-air wireless communications.

[0065] The one or more emulation devices may perform the one or more, including all, functions while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communications via RF circuitry(e.g., which may include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0066] Embodiments are described herein for Application Function (AF) initiated Vertical Federated Learning (VFL). An AF may request 5G Core (5GC) to provide recommended sample sets for model training with requested sample size and / or requested selection criteria. Based on the request, a Network Exposure Function or Network Data Analytics Function (NEF / NWDAF) may collect data from Network Functions (NFs) relating to the requested selection criteria. Based on collected Data, the NEF / NWDAF may select samples of WTRUs to satisfy the selection criteria. The NEF / NWDAF may respond to the AF with a list of WTRUs as samples (e.g., by Generic Public Subscription Identifier (GPSI), etc.). The AF and 5GC may perform VFL based on the negotiated / selected samples and features.

[0067] Federated learning among multiple Network Data Analytics Functions (NWDAFs) may be specified by 3GPP. 3GPP may specify how NWDAF functions including model training functions can leverage Federated Learning (FL) techniques to train a machine learning (ML) model.

[0068] For Horizontal Federated Learning, a NWDAF function enabled for federated learning may register perform registration with a Network Repository Function (NRF). For example, the NWDAF may provide the NRF with a Network Function (NF) profile of the NWDAF, information for supported analytic service (e.g., Analytics ID(s)), Address information of the NWDAF, Service Area, and / or its capability for Federated Learning. This registered information may be utilized to find proper NWDAF functions to join federated learning for analytics services with candidate ML models and requested service areas.

[0069] Model Filter information may be defined to indicate the conditions when an ML model is requested for analytics service and the target(s) of the ML model, such as specific WTRU(s), a group of WTRUs, or any WTRU.

[0070] For Horizontal Federated Learning, Federated Learning (FL) Server Network Data Analytics Functions (NWDAF) and FL Client NWDAF may be defined. When an analytic service is requested, federated learning may be requested to a FL Server NWDAF with ML model Accuracy. The FL Server NWDAF may discover and select proper FL Client NWDAF(s) for specific analytics service with requested ML model(s) based on selection criteria (e.g., at some service area, Network Function (NF) types of data, source from which NWDAF collects data for local model training, interested time period, etc.). The FL Server NWDAF may also provide the FL Client NWDAF with local ML models and request FL Client NWDAF to perform the local model training. Each FL Client NWDAF may collect its local data, perform local model trainingwith its own data, and report the interim local ML model information to the FL Server NWDAF. FL Server NWDAF may update a global ML model based on the aggregated local ML models and provide the proper global ML model for the requested analytics service.

[0071] Embodiments are described herein for use case for vertical federated leaning in 5GC. Vertical Federated Learning may be a federated learning method in which multiple parties perform training on data sets that share the same sample space but differ in feature space. Because of this characteristic, an alignment in sample and feature spaces among participating entities may be usually required before applying VFL (e.g., as part of VFL initialization). VFL may allow the performance of joint training without exposing raw data, with each entity owning its own model. Therefore, VFL may be a proper method for federated learning when privacy protection is required among the parties performing training.

[0072] Network Analytics Features in 5GC may be provided with VFL among Application Functions (AFs) and Network Function (NFs) in 5GC. In order to support VFL for analytics derivation, sample and feature alignment between the entities participating in VFL may be required (e.g., as part of VFL initialization).

[0073] In one example, 5GC may provide observed service experience analytics as VFL.

[0074] For observed service experience analytics service by NWDAF, for each WTRU interested, an AF may provide service quality and service experience data as input data. The 5GC NF may provide network data in Quality of Service (QoS) flow level as input data. The Operations, Administration, and Maintenance (QAM) system may provide WTRU level Network (NW) data relating to the QoS profile.

[0075] However, when the NWDAF or AF initiates VFL training process for observed service experience analytics, it may be desired not to exchange raw data directly between NWDAF and an external AF. For example, this may be because the NWDAF is in the Public Land Mobile Network (PLMN) and the AF is outside the PLMN, and the user data has high privacy protection needs.

[0076] Moreover, the NWDAF and AF may have different features of the same sample identity for local training, though the application of VFL among two entities requires alignment of samples and features.

[0077] Additionally, because the inference for VFL is also a distributed inference, no raw data may be shared in the inference as well as in the training. Each entity may use local data to determine the inference, and the output may be gathered to get the final result.

[0078] The AF or 5GC may trigger VFL for network analytics service (e.g., observed service experience analytics based on VFL). Each entity involved in VFL may perform local trainingbased on locally collected data. However, the AF and 5GC may need to align the samples and features before performing VFL operations (e.g., local training).

[0079] For better model training reliability, a representative dataset for model training may be selected. A representative sample set may be selected for network analytics service based on VFL. Therefore, there may be an issue on how a proper sample set is selected for VFL, where the sample set represents the dataset for model training reliability. The embodiments described herein present solutions for this problem for VFL.

[0080] When a Network Analytic Service for Vertical Federated Learning is requested (e.g., in the request for sample alignment), sample alignment may be requested by a consumer NF. Either a 5GC NF (e.g., NWDAF / NEF) or AF may also request sample alignment when Vertical Federated Learning is triggered (e.g., initialized) by a 5GC NW (e.g., NWDAF / NEF) or AF.

[0081] When sample alignment for VFL is requested, sampling of WTRUs (or any other relevant sample, for example, a network slice or data network) may be requested to 5GC with selection criteria for sampling WTRUs. When sampling of WTRUs with selection criteria is requested, 5GC may collect data from relevant NFs based on the selection criteria and requested time period for data collection and determine a list of WTRUs as samples satisfying the selection criteria.

[0082] Selection criteria for sampling WTRUs may include one or more of following conditions: requested sample sizes, requested conditions, requested aspects, and / or requested Dataset Statistical Properties. Selection criteria may indicate to select each WTRU in the sample that satisfies the requested conditions. Selection criteria may indicate to select one or more (e.g., all) WTRUs in a sample, if data of the WTRUs in the sample satisfies the requested aspects (e.g., requested statistical distribution). For example, the AF or NWDAF may want to select WTRUs as samples based on requested aspects, such as that the WTRUs have a WTRU separation distance. The WTRU separation distance may be the minimum distance between the WTRUs in the group. Examples of requested Dataset Statistical Properties may include uniform distribution, with or without outliers, etc.

[0083] If a certain WTRU list (e.g., target WTRUs) is provided with selection criteria, the list of WTRUs (e.g., target WTRUs) are verified. Verifying the list of WTRUs may comprise determining whether data of target WTRUs for the requested aspects satisfy the requested statistical properties. The list of WTRUs may be updated (e.g., down-selection list of WTRUs, adding additional list of WTRUs not in the list) to satisfy the requested statistical properties.

[0084] The requested conditions may include NW slice information, Application information, data network name (DNN), Data type (e.g., IPv4, IPv6, etc.), Interested location information, etc.

[0085] User consent (e.g., on data collection for the requested Analytics for the VFL model) may also be considered as requested conditions.

[0086] The requested aspects may include one or more of the followings: used RATs (e.g., whether 5G, LTE, N3GPP are used); requested QoS range (e.g., range of average or maximum bitrates, range of average or maximum delays, range of average or maximum packet error rate); protocol data unit (PDU) session type (e.g., Session and Service Continuity (SSC) mode 1 , 2, or 3); use remote Data servers or Edge servers; list of Data Network Access Identifiers (DNAIs); and / or interested service areas.

[0087] Embodiments are described herein for procedures for AF-initiated VFL operation for NW analytics. FIG. 2 depicts a flowchart illustrating an exemplary procedure of Application Function (AF) initiated Vertical Federated Learning (VFL) operation for Network (NW) Analytic at 200. At 202, the AF 230 may be triggered to perform Network Analytic Service with Vertical Federated Learning. Based on the application, the requested target user’s characteristics for machine learning may be determined. For example, an application utilizing adaptive data rate such as voice call and / or video streaming service based on machine learning will require users to be in some range of channel condition covering low data rate and high data rate during machine learning process. Based on the requested target user’s characteristics for machine learning, the selection criteria for sampling of WTRUs may be determined.

[0088] Once the AF 230 decides to use VFL with 5GC for Network Analytic Service, AF 230 may send a Service Request for VFL initialization or alignment (e.g., at 204). The service request may include Requested Analytics Service’s information (e.g., alignment information), Candidate ML models for the requested analytic service, Lists of features to be used in the requested ML model as input data for federated learning. The AF 230 may include Requested Service Area information and / or Time period information for data collection. The AF 230 may request 5GC to provide recommended sample set of WTRUs (e.g., implicitly) by including selection criteria for sampling WTRUs (e.g., in the request for alignment information). The selection criteria for sampling WTRUs may include requested size of sample, requested conditions, requested aspects, and / or requested dataset statistical properties. The requested conditions may include an indication of which requested conditions should be satisfied by each WTRU in the sample (e.g., requested Network Slice information, area of interest, etc.). The requested aspects may include an indication of which data from sampled WTRUs in the aspect satisfies the requested statistical properties (e.g., average data rate range, location in the area of interest, etc.). Examples of requested dataset statistical properties may include uniform distributions, with or without outliers, etc.

[0089] The AF 230 may provide a list of target WTRUs with the Selection criteria (e.g., in the request for alignment information). If the list of target WTRUs and the Selection Criteria are received together, the NWDAF 234 / NEF 232 may verify whether sample WTRUs satisfy the selection criteria, down-select the list of WTRUs from sample WTRU to satisfy the selection criteria, and / or provide a list of additional WTRUs that were not part of the initial list to satisfy the selection criteria. For example, additional list of WTRUs may be selected when the determined Sample size needs to be increased to meet the requirement, or when additional WTRUs need to be provided to make the dataset distribution satisfy the request statistical distribution. The additional WTRUs may not be initially in the target WTRU list. The dataset distribution may have the WTRUs as samples.

[0090] At 206, after receiving Service Request for VFL initialization, the network exposure function (NEF) 232 may (e.g., as part of an alignment procedure) query a network repository function (NRF) to discover a Network Data Analytics Function (NWDAF) 234 to perform federated learning for the requested Analytics Service (e.g., at the requested Service Area, if Requested Service Area information is included in the request from AF 230). Additionally, or alternatively, NEF 232 may send a request for VFL initialization to the selected NWDAF 234. The NEF 232 may use the Feature Set provided by the AF to derive possible samples to be aligned. The request may include information received previously.

[0091] At 208, after receiving a request for VFL initialization, (e.g., as part of the alignment procedure), the NWDAF 234 may collect data for the requested Analytic Service from the relevant 5GC NFs (e.g., unified data management (UDM) 238, AMF 236, SMF 240, UPF 242, etc.) and check the availability of requested features. When the NWDAF 234 receives selection criteria for sampling WTRUs, the NWDAF 234 may build a list of candidate WTRUs and collect data for the candidate WTRUs. The list of candidate WTRUs may include WTRUs that satisfy the requested conditions in the selection criteria. The collected data for the candidate WTRUs may be collected from the relevant 5GC NFs (e.g., UDM 238, AMF 236, SMF 240, UPF 242, etc.) relating to the requested features and the requested aspects in the selection criteria.

[0092] For example, (e.g., as part of the alignment procedure), the NWDAF 234 may contact Access and Mobility Management Function (AMFs) 236 (e.g., at 210) which serve the area of interest to build the list of WTRUs in the area. The NWDAF 234 may (e.g., as part of the alignment procedure), query a network slice selection function (NSSF) or UDM 238 (e.g., at 212) to query Network Slice information of the WTRU in the list, collect data for the features relating to NW slice information, and / or verify whether each WTRU in the list satisfy the condition for Network Slice (e.g., if NW slice information is included in the requested conditions).The NWDAF 234 may (e.g., as part of the alignment procedure), contact an SMF 240 (e.g., at 214) to query for PDU session related information, collect data for the features relating to PDU session, and / or verify whether the UE in the list satisfy any PDU session related condition (e.g., DNN, SSC mode, etc.). The NWDAF 234 may (e.g., as part of the alignment procedure), contact UPF and radio access network (RAN) 242 (e.g., at 216) for User Plan related information for the WTRUs in the list, collect data for the features relating to User Plane, and verify whether the WTRUs in the list satisfy any User Plane related condition (e.g., error rate, data rate, etc.).

[0093] The NWDAF 234 may contact a gateway mobile location center (GMLC) to query location information of each WTRU in the list.

[0094] If requested time period is included previously, the data collection may be performed within the requested time period.

[0095] At 218, (e.g., as part of the alignment procedure), based on collected data, the NWDAF 234 may select samples of WTRUs that satisfy the selection criteria. In examples, each WTRU may satisfy the requested conditions and data of the WTRUs for requested aspects may satisfy the requested statistical distribution. The NWDAF 234 may select at least as many WTRUs as the requested size of samples. The NWDAF 234 may send a response for VFL initialization to the NEF. The response may include an available ML model for the requested analytic ID and / or a list of available features. The list of available features may be omitted if all the requested features are supported. The response may also include a recommended list of WTRUs as samples.

[0096] If the size of the list of samples of WTRUs is less than the requested size of samples, the service request from AF 230 may be rejected with a reject code representing that the requested sample size is not satisfied. In other embodiments, when the size of list of samples of WTRUs is less than the requested size of samples, the list is reported to the AF 230 and the AF 230 may determine whether the AF 230 proceeds to VFL operation with the samples or not.

[0097] In another embodiment, NEF 232 may perform the previous procedures. That is to say, (e.g., as part of the alignment procedure), at 218, the NEF 232 may select samples of WTRUs that satisfy the selection criteria. The NEF 232 may select at least as many as the requested size of the samples.

[0098] At 220, the NEF 232 may send a Service Response for VFL initialization to the AF 230 as a response of the request received previously. The Service Response may include an indication whether the requested ML model for the requested analytics service is available, a list of available ML models for the requested analytic service, a list of available features (e.g.,parameters, parameters for VFL alignment) for the available ML models as input data which may be omitted if all the requested features are supported, and / or a recommended list of WTRUs as samples (e.g., as part of the parameters for VFL alignment). When the list of WTRUs as sample is included, the WTRU’s identity which can be understood by AF 230 is included (e.g., by using GPSI translation service, etc.). For each available ML model, proposed hyperparameters for ML may be added.

[0099] Optionally, at 222, the AF 230 may send a confirmation for VFL initialization. The confirmation may include a list of available features and a list of WTRUs as samples if they are updated from the list received previously. The response may include a selected ML model and selected hyperparameters for the selected ML model. For example, AF 230 may down select a list of WTRUs from the recommended list of WTRUs as samples based on the availability of data for the features of the WTRUs for model training in AF 230.

[0100] At 224, the AF 230 may perform vertical federated learning with the 5GC for the analytic service based on the available ML model or selected ML model, the selected hyperparameters for the selected ML model, and / or the list of available feature and sample of WTRUs received and updated previously. In examples, the AF 230 may send an initiation message to the NEF 232 for VFL, including the list of available features and / or list of WTRUs for VFL training. After performing federated learning, the AF 230, NEF 232, and 5GC may share their locally trained results. The data collected previously may be utilized for local training in 5GC if no other request is received for data collection.

[0101] Based on the shared locally trained result, the AF 230 may determine an ML model with the trained results from the AF 230 and the 5GC. If the performance (e.g., conformance level of output of ML model) of the ML model based on trained result is not good enough, the AF 230 may request 5GC updated VFL with an updated ML model (e.g., using the determined ML model), updated features, and / or updated samples. Based on the request for updated VFL for AF 230, 5GC may perform local training of the requested updated ML model and share the locally trained result. This procedure may repeat until developing ML model with satisfying the requested performance.

[0102] In another embodiment, the AF and the 5GC may negotiate the time period and / or sampling ratio for data collection for the negotiated features and samples of WTRUs. Additionally, or alternatively, the AF and the 5GC may use the newly collected data for local training from AF and 5GC.

[0103] Embodiments are described herein for procedures for consumer Network Function (NF) initiated VFL operation for NW Analytics. Consumer NFs may request VFL operation for NWanalytics. In this case, consumer NF may provide selection criteria for sampling WTRUs for the requested VFL for NW analytics.

[0104] When receiving service request for VFL operation for NW analytics from consumer NF, NWDAF / NEF may perform any of the procedures described previously in order to determine ML model, features, and sample WTRUs based on received selection criteria.

[0105] Based on the selected sample WTRUs, features, and ML model, NWDAF / NEF may initiate VFL operation for NW analytics with the AF. The NWDAF / NEF may include a list of candidate ML models, features, and / or a list of sample WTRUs. The AF may respond to indicate whether the AF may join the VFL and whether AF may provide updated list of sample WTRUs and features based on the availability of data for the requested features and samples. For example, when some of the WTRU’s data for the requested features is not available to the AF, the AF may drop the WTRU and down select sample WTRUs based on the data availability for the requested features.

Claims

CLAIMS:

1. A network node comprising a processor and memory, wherein the processor and memory are configured to: receive a request for alignment information for vertical federated learning (VFL), wherein the request for alignment information comprises sample selection criteria; perform an alignment procedure with a network function based on the sample selection criteria comprised in the alignment information; and send a response to the request for alignment information, the response indicating parameters for VFL alignment, wherein the parameters for VFL alignment comprise a list of wireless transmit / receive units (WTRUs) recommended for VFL training.

2. The network node of claim 1 , wherein the processor and memory are configured to receive the request for alignment information from an application function (AF).

3. The network node of claim 1 , wherein the parameters for VFL alignment indicate an analytic service and an associated machine learning (ML) model.

4. The network node of claim 1 , wherein the request for alignment information indicates a request for a set of recommended WTRUs for VFL training for a machine learning (ML) model.

5. The network node of claim 1 , wherein the sample selection criteria comprises network slice information, location or area information, a date range, or a statistical condition.

6. The network node of claim 1 , wherein the alignment procedure comprises a determination of whether a WTRU of the list of WTRUs recommended for VFL training satisfies the sample selection criteria comprised in the alignment information.

7. The network node of claim 1 , wherein the alignment procedure comprises a determination to exclude a WTRU from the list of WTRUs recommended for VFL training, based on a determination that the WTRU does not satisfy the sample selection criteria comprised in the alignment information.

8. The network node of claim 1 , wherein the processor and memory are further configured to: receive an initiation message for VFL training.

9. The network node of claim 8, wherein the initiation message comprises a list of available features, or a list of WTRUs selected for use in VFL training.

10. The network node of claim 9, wherein the processor and memory are further configured to: perform VFL operations using the list of available features or list of WTRUs selected for use in VFL training, wherein the VFL operations include sharing a locally trained VFL result.

11. A method to be performed by a cellular network node, the method comprising: receiving a request for alignment information for vertical federated learning (VFL), wherein the request for alignment information comprises sample selection criteria; performing an alignment procedure with a network function based on the sample selection criteria comprised in the alignment information; and sending a response to the request for alignment information, the response indicating parameters for VFL alignment, wherein the parameters for VFL alignment comprise a list of wireless transmit receive units (WTRUs) recommended for VFL training.

12. The method node of claim 11 , further comprising: receiving the request for alignment information from an application function (AF).

13. The method of claim 11 , wherein the parameters for VFL alignment indicate a machine learning (ML) model.

14. The method of claim 11 , wherein the request for alignment information indicates a request for a machine learning (ML) model or a request for a set of recommended WTRUs for VFL training.

15. The method of claim 11 , wherein the sample selection criteria comprises network slice information, location or area information, a date range, or a statistical condition.

16. The method of claim 11 , wherein performing the alignment procedure comprises determining whether a WTRU of the list of WTRUs recommended for VFL training satisfies the sample selection criteria comprised in the alignment information.

17. The method of claim 11 , wherein performing the alignment procedure comprises excluding a WTRU from the list of WTRUs recommended for VFL training, based on a determination that the WTRU does not satisfy the sample selection criteria comprised in the alignment information.

18. The method of claim 11 , further comprising: receiving an initiation message for VFL training.

19. The method of claim 18, wherein the initiation message comprises a list of available features, or a list of WTRUs selected for use in VFL training.

20. The method of claim 19, further comprising: performing VFL operations, wherein the VFL operations include sharing a locally trained VFL result.

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