HeaPS: Heterogeneity-Aware Participant Selection for Efficient Federated Learning

Federated learning enables collaborative model training among numerous clients. However, existing participant/client selection methods fail to fully leverage the advantages of clients with excellent computational or communication capabilities. In this paper, we propose HeaPS, a novel Heterogeneity-aware Participant Selection framework for efficient federated learning. We introduce a finer-grained global selection algorithm to select communication-strong leaders and computation-strong members from candidate clients. The leaders are responsible for communicating with the server to reduce per-round duration, as well as contributing gradients; while the members communicate with the leaders to contribute more gradients obtained from high-utility data to the global model and improve the final model accuracy. Meanwhile, we develop a gradient migration path generation algorithm to match the optimal leader for each member. We also design the client scheduler to facilitate parallel local training of leaders and members based on gradient migration. Experimental results show that, in comparison with state-of-the-art methods, HeaPS achieves a speedup of up to 3.20× in time-to-accuracy performance and improves the final accuracy by up to 3.57%. The code for HeaPS is available at https://github.com/Dora233/HeaPS.