Pricing
FedGR: Genetic Algorithm and Relay Strategy Based Federated Learning
The proposed FedGR framework groups clients according to their local data distribution using genetic algorithm and relay strategy and significantly outperforms other state-of-the-art federated learning algorithms on various image classification tasks.
Abstract
Federated learning (FL) is a privacy-preserving distributed machine learning approach that enables multiple parties to collaboratively train machine learning models without sharing local data. However, compared with the models trained on independent and identically distributed (IID) data, existing methods still face significant degradation in model performance when running on non-IID data. To solve this problem, we propose a federated learning framework based on genetic algorithm and relay strategy in this paper. The framework groups clients according to their local data distribution using genetic algorithm. After obtaining the optimal grouping result, a relay strategy is used to train and aggregate models within each group. Extensive experiments on three benchmark datasets show that FedGR significantly outperforms other state-of-the-art federated learning algorithms on various image classification tasks. The source code is available at https://github.com/zyfhylyh/FedGR.