  Federated learning is a method of training a global model from decentralized
data distributed across client devices. Here, model parameters are computed
locally by each client device and exchanged with a central server, which
aggregates the local models for a global view, without requiring sharing of
training data. The convergence performance of federated learning is severely
impacted in heterogeneous computing platforms such as those at the wireless
edge, where straggling computations and communication links can significantly
limit timely model parameter updates. This paper develops a novel coded
computing technique for federated learning to mitigate the impact of
stragglers. In the proposed Coded Federated Learning (CFL) scheme, each client
device privately generates parity training data and shares it with the central
server only once at the start of the training phase. The central server can
then preemptively perform redundant gradient computations on the composite
parity data to compensate for the erased or delayed parameter updates. Our
results show that CFL allows the global model to converge nearly four times
faster when compared to an uncoded approach
