FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging
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Federated learning (FL) is an emerging learning paradigm to tackle massively distributed data. In Federated Learning, a set of clients jointly perform a machine learning task under the coordination of a server. The FedAvg algorithm is one of the most widely used methods to solve Federated Learning problems. In FedAvg, the learning rate is a constant rather than changing adaptively. The adaptive gradient methods show superior performance over the constant learning rate schedule; however, there is still no general framework to incorporate adaptive gradient methods into the federated setting. In this paper, we propose FedDA, a novel framework for local adaptive gradient methods. The framework adopts a restarted dual averaging technique and is flexible with various gradient estimation methods and adaptive learning rate formulations. In particular, we analyze FedDA-MVR, an instantiation of our framework, and show that it achieves gradient complexity Õ(ϵ^-1.5) and communication complexity Õ(ϵ^-1) for finding a stationary point ϵ. This matches the best known rate for first-order FL algorithms and FedDA-MVR is the first adaptive FL algorithm that achieves this rate. We also perform extensive numerical experiments to verify the efficacy of our method.
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