  Stragglers' effects are known to degrade FL performance. In this paper, we
investigate federated learning (FL) over wireless networks in the presence of
communication stragglers, where the power-constrained clients collaboratively
train a global model by iteratively optimizing a local objective function with
their local datasets and transmitting local model updates to the central
parameter server (PS) through fading channels. To tackle communication
stragglers without dataset sharing or prior information about the network at
PS, we propose cooperative gradient coding (CoGC) for semi-decentralized FL to
enable the exact global model recovery at PS. Furthermore, we conduct a
thorough theoretical analysis of the proposed approach. Namely, an outage
analysis of the proposed approach is provided, followed by a convergence
analysis based on the failure probability of the global model recovery at PS.
Nevertheless, simulation results reveal the superiority of the proposed
approach in the presence of stragglers under imbalanced data distribution.
