A Buffered Semi-Asynchronous Mechanism with MAB for Efficient Federated Learning

Federated learning can leverage abundant data generated by scattered client devices without impairing their data privacy. However, heterogeneous properties of devices may cause a long single round duration in synchronous federated learning and stale gradient of clients in asynchronous federated learning. To solve these issues, we propose a buffered semi-asynchronous federated learning with efficient client selection strategy, named BSACS-FL. Specifically, we introduce a buffer mechanism which is used to store models that have not yet been aggregated on client side to improve clients computing resource utilization. In addition, we adopt Multi-Armed Bandit (MAB) method to select a proper set of clients to upload new local models based on the waiting time of local model in the buffer and the learned training capability of clients. The novel BSACS-FL algorithm boosts time efficiency and improves client computing resource utilization, which achieves more effective model aggregations, leading to a faster convergence with high model accuracy. Extensive experiments based on both image classification and human activity recognition tasks demonstrate the superior performance of BSACS-FL, e.g., improving the model accuracy by 19% compared to the FedAvg.