Gradient Compensation Enabled Federated Learning for Unreliable Wireless Links
Zhixiong Chen, Wenqiang Yi, Yun Hee Kim, Arumugam Nallanathan · 2024
Wireless federated learning (FL) faces significant challenges due to limited wireless resources and unreliable channels. To cope with these challenges, this work proposes a gradient compensation-based FL approach (FL-GC), in which the edge server estimates the local gradients of transmission failure and unselected clients by first-order Taylor approximation based on previously received local gradients. We then theoretically analyze the convergence bound, which reveals that selecting clients with large local gradient staleness helps reduce the estimation error and improve learning performance. Based on this, we jointly optimize the client selection and resource allocation strategies to enhance the FL performance under resource-limited wireless networks. Simulation results under a typical data heterogeneity scenario demonstrate the efficacy of our proposed scheme in mitigating the adverse effects of unreliable transmission and limited resources. It improves 7.34% model accuracy compared to the considered benchmarks and is able to save 42.5% training time to achieve the target accuracy.