Cooperative Communication Based Gradient Coding for Wireless Federated Learning

We investigate wireless federated learning (FL) in the presence of stragglers, where the power-constrained wireless devices collaboratively train a global model on their local datasets %within a time constraint and transmit local model updates through fading channels. To tackle stragglers resulting from link disruptions without requiring accurate prior information on connectivity or dataset sharing, we propose a gradient coding (GC) scheme based on cooperative communication. Subsequently, we conduct an outage analysis of the proposed scheme, based on which we conduct the convergence analysis. The simulation results reveal the superiority of the proposed strategy in the presence of stragglers, especially in low signal-to-noise ratio (SNR) scenarios.