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FedCM: Federated Learning with Client-level Momentum
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FedCM: Federated Learning with Client-level Momentum
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Federated Learning is a distributed machine learning approach which enables model training without data sharing. In this paper, we propose a new federated learning algorithm, Federated Averaging with Client-level Momentum (FedCM), to tackle problems of partial participation and client heterogeneity in real-world federated learning applications. FedCM aggregates global gradient information in previous communication rounds and modifies client gradient descent with a momentum-like term, which can effectively correct the bias and improve the stability of local SGD. We provide theoretical analysis to highlight the benefits of FedCM. We also perform extensive empirical studies and demonstrate that FedCM achieves superior performance in various tasks and is robust to different levels of client numbers, participation rate and client heterogeneity.
Forward citations
Cited by 5 Pith papers
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Low-frequency components of client-side SAM perturbations carry most inter-client disagreement; high-pass filtering them yields more consistent federated updates and higher accuracy under non-IID data.
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