Federated Learning Client Selection via Local Evaluation
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Solution Overview
Problem
Conventional federated learning methods face inefficiencies in selecting client apparatuses for model updates, leading to inaccurate models and increased communication costs, particularly when all client apparatuses participate, and centralized selection processes raise privacy concerns.
Innovation Solution
The method involves each information processing apparatus collecting local data, generating a local model, calculating an evaluation value based on an evaluation index, and transmitting it to nearby apparatuses via proximity communication, allowing for autonomous distributed selection of apparatuses with high evaluation values to send their local models to a server for integration, thus improving efficiency and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all client apparatuses participate in federated learning, then model accuracy may improve, but communication costs and system complexity increase
Solution Approach 1:
The patent implements selective participation where only client apparatuses with high evaluation values (top performers) are chosen to transmit their local models to the server. This partial action principle reduces the number of participants from all clients to a selective subset, thereby lowering communication costs while maintaining model accuracy through the contribution of high-quality local models
Solution Approach 2:
The patent changes the selection parameter from random or uniform participation to evaluation-based selection. Each client apparatus calculates an evaluation value based on its local data characteristics and model performance, and the server uses these evaluation values to determine which clients should participate. This parameter change enables intelligent filtering of participants to optimize the balance between accuracy and communication efficiency
2Ease of operation
If centralized selection process is used to choose client apparatuses, then coordination is improved, but privacy concerns increase
Solution Approach 1:
The patent extracts the privacy-sensitive information (local data and detailed model parameters) from the client apparatuses and keeps it locally. Only non-sensitive evaluation values are transmitted to the server for selection purposes. This extraction principle separates the selection function from the sensitive data, enabling centralized coordination without exposing private information
Solution Approach 2:
The evaluation value acts as an intermediary that conveys information about client apparatus quality without revealing sensitive local data. The server uses this intermediary metric to make selection decisions, thereby coordinating the federated learning process while maintaining a privacy barrier between the server and client apparatuses
3Reliability
If client apparatuses transmit local models frequently, then model convergence improves, but communication overhead increases
Solution Approach 1:
The patent applies partial action by having only selected client apparatuses (those with high evaluation values) transmit their local models to the server, rather than all clients transmitting in every round. This reduces communication overhead while maintaining convergence through the contribution of high-quality models from selected participants
Solution Approach 2:
The patent implements preliminary action by having client apparatuses calculate evaluation values before the selection and transmission process. This preliminary evaluation allows the server to identify and select only those clients whose local models are most likely to contribute to convergence, thereby reducing unnecessary communication overhead while preserving convergence reliability
Data Source
AI summary
Each of information processing apparatuses generates a local model by performing machine learning of a global model using local data. Each of the information processing apparatuses calculates an evaluation value according to an evaluation index. The information processing apparatuses exchange the calculated evaluation values by proximity communication. Each of the information processing apparatuses determines whether the evaluation value of the information processing apparatus has a high evaluation or not based on a result of comparison between the evaluation value and evaluation values of the other information processing apparatuses. At least one of the information processing apparatuses that has determined that the evaluation value has the high evaluation transmits local model information indicating the generated local model to a server apparatus.