  Federated learning (FL) is a promising framework for privacy-preserving
collaborative learning, where model training tasks are distributed to clients
and only the model updates need to be collected at a server. However, when
being deployed at mobile edge networks, clients may have unpredictable
availability and drop out of the training process, which hinders the
convergence of FL. This paper tackles such a critical challenge. Specifically,
we first investigate the convergence of the classical FedAvg algorithm with
arbitrary client dropouts. We find that with the common choice of a decaying
learning rate, FedAvg oscillates around a stationary point of the global loss
function, which is caused by the divergence between the aggregated and desired
central update. Motivated by this new observation, we then design a novel
training algorithm named MimiC, where the server modifies each received model
update based on the previous ones. The proposed modification of the received
model updates mimics the imaginary central update irrespective of dropout
clients. The theoretical analysis of MimiC shows that divergence between the
aggregated and central update diminishes with proper learning rates, leading to
its convergence. Simulation results further demonstrate that MimiC maintains
stable convergence performance and learns better models than the baseline
methods.
