Federated Learning Weight Compression for Wireless Parameter Updates
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Solution Overview
Problem
Current federated learning methods in wireless communication systems face inefficiencies in data compression, particularly in selecting appropriate compression methods based on the degree of learning, which affects the accuracy and speed of parameter updates, especially as the number of participating devices increases.
Innovation Solution
The method involves determining a weight compression method for user equipment (UEs) based on the difference between global and local parameters, using either low-level compression or important-aware compression, depending on the convergence of the learning process, and transmitting updated local parameters to a server, which adjusts its compression state information accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is applied to reduce communication overhead, then communication efficiency is improved, but parameter update accuracy deteriorates
Solution Approach 1:
The patent applies dynamic compression where the compression method and level are adjusted based on the learning stage. In early training stages, lower compression is used to maintain accuracy, while in later stages, higher compression is applied as the model converges. This is achieved by monitoring convergence metrics and adapting the compression strategy accordingly, resolving the contradiction between communication efficiency and accuracy.
Solution Approach 2:
The patent changes compression parameters (compression ratio, quantization bits) based on the learning progress and channel conditions. By dynamically adjusting these parameters, the system optimizes the balance between reducing communication overhead and maintaining sufficient parameter update accuracy for effective federated learning.
2Loss of substance
If compression level is increased to reduce communication overhead, then communication overhead is reduced, but learning accuracy deteriorates
Solution Approach 1:
The system dynamically adjusts compression levels based on the federated learning convergence status. When the model shows significant changes between iterations, lower compression is applied to preserve accuracy. When convergence is detected, higher compression levels are used to reduce overhead, thus balancing communication efficiency and learning accuracy throughout the training process.
Solution Approach 2:
The patent performs preliminary compression testing and convergence assessment before applying full compression. By evaluating the learning state in advance, the system determines the appropriate compression level to apply, ensuring that accuracy requirements are met before maximizing compression to reduce communication overhead.
3Device complexity
If uniform compression is applied to all parameters, then implementation complexity is reduced, but important parameter information is lost
Solution Approach 1:
The patent applies different compression strategies to different parameters based on their importance. Critical parameters that significantly impact model performance are compressed with lower ratios to preserve information, while less important parameters use higher compression. This selective approach maintains learning accuracy while managing communication overhead, resolving the contradiction between implementation complexity and information preservation.
4Productivity
If compression method is changed based on learning stage, then learning efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements dynamic compression method selection that adapts to the learning stage. In early stages, simpler compression methods are used, while in later stages, more advanced compression techniques are applied as the model converges. The system monitors convergence metrics and automatically adjusts the compression strategy, improving learning efficiency while managing system complexity through rule-based adaptation.
Data Source
AI summary
The present disclosure provides a method for one user equipment (UE) to perform federated learning with a plurality of UEs in a wireless communication system. More specifically, the method performed by the one UE comprises receiving, from a server, a channel state information reference signal (CSI-RS); transmitting, to the server, channel state information (CSI) calculated based on the CSI-RS; receiving, from the server, compression state information for determining a weight compression method of the one UE based on (i) information on a global parameter for the federated learning and (ii) channel state information of each of channels between the server and the plurality of UEs; determining the weight compression method based on (i) a difference between the global parameter and a global parameter received before a reception of the global parameter and (ii) the compression state information; and transmitting, to the server, a local parameter updated based on the determined weight compression method.