Federated Learning Terminal Grouping for Wireless Signal Transmission
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Solution Overview
Problem
Current wireless communication systems face inefficiencies in federated learning, particularly when multiple terminals participate, due to high overhead and traffic requirements for data characteristic identification, which complicates resource allocation and model synchronization.
Innovation Solution
The proposed solution involves a method and device for efficient federated learning based on grouping, where terminals receive configuration information, learn local models, and transmit weight messages to determine resource allocation and perform air-computation-based learning using a split local model, allowing for reduced traffic and synchronized parameter aggregation across groups with similar data distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple terminals participate in federated learning, then learning capability is improved, but data transmission overhead increases
Solution Approach 1:
The patent divides terminals into multiple groups based on data distribution characteristics and assigns different resource allocation strategies to each group. This segmentation allows the system to manage federated learning efficiently by processing groups rather than handling all terminals individually, reducing overall communication overhead while maintaining learning capability across multiple participants.
Solution Approach 2:
The patent applies differentiated resource allocation strategies tailored to each group's specific data distribution characteristics. By considering local quality differences among groups (such as data non-identicality levels), the system optimizes transmission parameters and aggregation methods for each group, reducing unnecessary data transmission while preserving learning effectiveness.
2Measurement precision
If data characteristic identification is performed for all terminals, then model accuracy is improved, but traffic requirements increase
Solution Approach 1:
Instead of performing comprehensive data characteristic identification on all terminals, the patent applies partial analysis by focusing on grouping terminals based on observed data distribution patterns. The system identifies and processes only the necessary characteristics required for effective grouping, avoiding excessive traffic consumption while maintaining sufficient model accuracy through targeted analysis rather than exhaustive examination.
3Productivity
If resource allocation is optimized for each terminal, then learning efficiency is improved, but system complexity increases
Solution Approach 1:
The patent merges individual terminal optimizations into group-level strategies. By combining multiple terminals into groups with similar data characteristics, the system achieves learning efficiency comparable to individual optimization while significantly reducing complexity. The group-based approach allows unified resource allocation decisions that apply to multiple terminals simultaneously, eliminating the need for separate optimization logic for each terminal.
Data Source
AI summary
Disclosed herein is a method of operating a terminal according to an embodiment, including: receiving, by the terminal, federated learning-related configuration information; learning, by the terminal, a local model based on the federated learning-related configuration information; receiving, by the terminal, a local model weight request message; transmitting a first response message based on the received weight request message; receiving information associated with a total local model based on the first response message; transmitting a second response message based on the received information associated with the total local model; receiving resource allocation-related information based on the second response message; and performing federated learning based on the received resource allocation-related information.