Empowering Clients: Self-Adaptive Federated Learning for Data Quality Challenges

Federated Learning (FL) enables collaborative model training across distributed clients, but the global model’s performance often degrades due to variable data quality and reliability at the local level. Previous approaches mitigate this by restricting or excluding contributions from certain clients, leading to wasted computation and communication resources for those disregarded. In this paper, we introduce FedSRC: Federated Learning with Self-Regulating Clients, a novel framework that optimizes resource use while safeguarding client anonymity. With FedSRC, clients autonomously assess their local training’s benefit to the global model using a lightweight checkpoint based on local test loss and a Refined Heterogeneity Index (RHI), deciding their participation in each FL round accordingly. Extensive evaluations across four datasets demonstrate that FedSRC achieves up to 30% savings in communication costs and 55% in computation costs, all while maintaining privacy and enhancing efficiency.