FedSTEP: Asynchronous and Staleness-Aware Personalization for Efficient Federated Learning

Personalized Federated Learning (PFL) aims to provide client-specific models that adapt to local data distributions while leveraging shared knowledge across clients. A common design in PFL is the head-representation architecture, which combines a shared global representation with a local head on each client. Although effective, deploying this architecture in real-world systems remains challenging due to the presence of stragglers and the high communication cost. To address these issues, we propose FedSTEP, a unified framework that integrates asynchronous training with dynamic communication sparsification. Specifically, it adaptively adjusts each client's local training duration and communication sparsity based on staleness, enabling more efficient coordination between local adaptation and global representation. This design mitigates the impact of stragglers and ensures robust performance in heterogeneous environments. We provide a theoretical analysis of the convergence behavior and communication efficiency of FedSTEP under standard assumptions. Extensive experiments on five public datasets demonstrate that FedSTEP consistently outperforms existing methods. It achieves up to 4.65% higher accuracy, a 3.68× speedup in training, and a 1.91× reduction in communication cost.