An Efficient Asynchronous Federated Learning Protocol for Edge Devices

Recent studies highlight the significant potential of edge computing and federated learning in advancing artificial intelligence. However, challenges such as unstable device performance and the heterogeneously distributed feature of local data pose threats to the efficiency of global model training. To address these issues, we propose an efficient federated edge learning protocol with key innovations: (i) Propose a timing query mechanism, which controls the impact of slow clients on interaction time and changes the parameter server from a passive receiver to an active querier to guarantee the aggregated subset size. (ii) Propose a screening supplementary strategy from the perspective of optimizing the quality of the data combination. (iii) Integrate the timing query mechanism and screening supplementary strategy in a flexible manner. This combination improves the interaction efficiency of global model training, focusing on both interaction time and aggregation update quality. Experiments show that while ensuring the convergence efficiency of each round and the global model performance, the interaction efficiency has been improved by 25% to 75%, thus ensuring the efficiency of federated learning.