Adaptive Homogeneity-Based Client Selection Policy for Federated Learning
Yiming Xie, Pinrui Yu, Geng Yuan, Xue Lin, Ningfang Mi · 2024
Federated learning (FL) is a distributed paradigm that enables multiple clients or edge devices to collaboratively train a model without sharing their local data. The FL system has to tackle a significant challenge due to the non-IID nature of client data. Traditional methods of client selection usually suffer from the problem of variable test accuracies as well as slow convergence because of their incapability in effectively handle data heterogeneity across clients. In this paper, we propose an Adaptive Homogeneity-Based Client Selection Policy (ASTraFL) to address this challenge. ASTraFL dynamically selects clients whose data distributions optimally complement the current state of the global model, focusing on increased homogeneity of the selected client data in each training round. Our experiments demonstrate that ASTraFL can accelerate convergence speed and ensure the learning process's robustness.