Accelerating Federated Learning via Sampling Anchor Clients with Large Batches
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Using large batches in recent federated learning studies has improved convergence rates, but it requires additional computation overhead compared to using small batches. To overcome this limitation, we propose a unified framework FedAMD, which disjoints the participants into anchor and miner groups based on time-varying probabilities. Each client in the anchor group computes the gradient using a large batch, which is regarded as its bullseye. Clients in the miner group perform multiple local updates using serial mini-batches, and each local update is also indirectly regulated by the global target derived from the average of clients' bullseyes. As a result, the miner group follows a near-optimal update towards the global minimizer, adapted to update the global model. Measured by ϵ-approximation, FedAMD achieves a convergence rate of O(1/ϵ) under non-convex objectives by sampling an anchor with a constant probability. The theoretical result considerably surpasses the state-of-the-art algorithm BVR-L-SGD at O(1/ϵ^3/2), while FedAMD reduces at least O(1/ϵ) communication overhead. Empirical studies on real-world datasets validate the effectiveness of FedAMD and demonstrate the superiority of our proposed algorithm.
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