Adaptive client selection and model aggregation for heterogeneous federated learning

Abstract

Federated Learning (FL) is a distributed machine learning method that allows multiple clients to collaborate on model training without sharing raw data. However, FL faces challenges with data heterogeneity, leading to reduced model accuracy and slower convergence. Although existing client selection methods can alleviate the above problems, there is still room to improve FL performance. To tackle these problems, we first propose a novel client selection method based on Multi-Armed Bandit (MAB). The method uses the historical training information uploaded by each client to calculate its correlation and contribution. The calculated values are then used to select a set of clients that can bring the most benefit, i.e., maximizing both model accuracy and convergence speed. Second, we propose an adaptive global model aggregation method that utilizes the local training information of selected clients to dynamically assign weights to local model parameters. Extensive experiments on various datasets with different heterogeneous settings demonstrate that our proposed method is effectively improving FL performance compared to several benchmarks.