Federated learning is a new distributed machine learning framework, where a
bunch of heterogeneous clients collaboratively train a model without sharing
training data. In this work, we consider a practical and ubiquitous issue when
deploying federated learning in mobile environments: intermittent client
availability, where the set of eligible clients may change during the training
process. Such intermittent client availability would seriously deteriorate the
performance of the classical Federated Averaging algorithm (FedAvg for short).
Thus, we propose a simple distributed non-convex optimization algorithm, called
Federated Latest Averaging (FedLaAvg for short), which leverages the latest
gradients of all clients, even when the clients are not available, to jointly
update the global model in each iteration. Our theoretical analysis shows that
FedLaAvg attains the convergence rate of $O(E^{1/2}/(N^{1/4} T^{1/2}))$,
achieving a sublinear speedup with respect to the total number of clients. We
implement FedLaAvg along with several baselines and evaluate them over the
benchmarking MNIST and Sentiment140 datasets. The evaluation results
demonstrate that FedLaAvg achieves more stable training than FedAvg in both
convex and non-convex settings and indeed reaches a sublinear speedup.

该研究提出了一种名为 FedLaAvg 的简单分布式非凸优化算法，用于解决在移动环境下采用联邦学习时客户端不连续可用的问题，并证明了其达到了收敛速度为 $ O (E^{1/2}/(N^{1/4} T^{1/2}))$ 的水平。