  Federated learning has received significant attention as a potential solution
for distributing machine learning (ML) model training through edge networks.
This work addresses an important consideration of federated learning at the
network edge: communication delays between the edge nodes and the aggregator. A
technique called FedDelAvg (federated delayed averaging) is developed, which
generalizes the standard federated averaging algorithm to incorporate a
weighting between the current local model and the delayed global model received
at each device during the synchronization step. Through theoretical analysis,
an upper bound is derived on the global model loss achieved by FedDelAvg, which
reveals a strong dependency of learning performance on the values of the
weighting and learning rate. Experimental results on a popular ML task indicate
significant improvements in terms of convergence speed when optimizing the
weighting scheme to account for delays.
