Adaptive Semi-Asynchronous Federated Learning over Wireless Networks

Owing to the heterogeneous computation and communication capabilities among clients, the synchronous model aggregation in wireless federated learning (FL) is susceptible to the straggler effect and exhibits low learning efficiency, while asynchronous aggregation encounters delayed gradients that lead to convergence errors and learning performance degradation. To address these obstacles, this work proposes an adaptive semi-asynchronous FL (ASAFL) approach to incorporate the strengths of synchronous and asynchronous FL while mitigating their inherent drawbacks. Specifically, the edge server dynamically adjusts the synchronous degree, i.e., the number of local gradients aggregated in each round, to strike a balance between learning latency and accuracy. Recognizing that data heterogeneity among clients may induce biased global model updating, we propose calibrating the global update by leveraging historical gradients received at the edge server from clients. Following that, we theoretically investigate the impact of synchronous degrees in different rounds on the convergence bound of ASAFL. The results imply that allocating more learning time to the later learning stages to increase the synchronous degree contributes to better learning performance. Based on this, we develop an adaptive synchronous degree control and resource allocation algorithm to enhance the learning performance of FL while adhering to the overall learning latency and wireless resources constraint. Numerical results on the MNIST and CIFAR-10 datasets demonstrate that the proposed approach is capable of attaining faster convergence speed and higher learning accuracy compared to the benchmark FL algorithms.