Analog Protocol Stack for Federated Learning Gradient Transmission
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Solution Overview
Problem
Current wireless communication systems, particularly New Radio (NR) networks, are not designed to process analog inputs from the physical layer, making it challenging to implement federated learning techniques efficiently, which require transmitting and aggregating analog gradient data for machine learning model updates.
Innovation Solution
An architectural enhancement to the NR network is introduced, incorporating an analog protocol stack that enables the computation and transmission of uplink channel-based analog gradient sums, allowing for efficient machine learning model updates by transmitting data in either analog or digital format, depending on network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If NR networks use traditional digital-only protocol stacks, then system compatibility and processing reliability are maintained, but the ability to process analog gradient data for federated learning is lost
Solution Approach 1:
The patent segments the protocol stack into distinct analog and digital components. The analog protocol stack handles analog gradient data transmission through dedicated analog signal processing paths, while the digital protocol stack maintains traditional digital communication functions. This segmentation allows the system to process analog gradient data without compromising existing digital communication capabilities, resolving the contradiction between adaptability and complexity.
2Productivity
If analog gradient data is transmitted over wireless channels, then communication overhead is reduced and federated learning efficiency is improved, but signal integrity and measurement precision deteriorate due to channel noise and fading
Solution Approach 1:
The patent implements feedback mechanisms where the base station receives analog gradient data from user equipment, processes it through the analog protocol stack, and provides aggregation results back to the network. This feedback loop enables continuous optimization of the federated learning process, allowing the system to maintain high efficiency while managing signal integrity through iterative improvements based on received gradient information.
3Productivity
If federated learning is implemented without analog protocol support, then system reliability and ease of operation are maintained, but communication overhead increases and learning speed decreases
Solution Approach 1:
The patent introduces an analog protocol stack as an intermediary layer between the physical wireless channel and the digital processing components. This intermediary enables efficient analog gradient data transmission while maintaining compatibility with existing digital network architectures. The analog protocol stack acts as a bridge that translates and processes analog signals without requiring complete redesign of the underlying digital infrastructure, thus improving model update speed without excessive complexity increase.
Data Source
AI summary
A method of wireless communication by a user equipment (UE) includes receiving, from a network device, a request to initiate gradient computations for a round of federated learning. The method also includes computing gradients in response to receiving the request to initiate gradient computation. The method further includes informing the network device of availability of the gradients. The method still further includes receiving, from the network device, information to enable transfer of the gradients to the network device. The method further includes transferring the gradients to the network device in response to receiving the information to enable transfer.