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parallel-federated-learning

topic1 events
papersTODAY 04:00 UTC

Study Analyzes Convergence of Sequential Federated Learning on Heterogeneous Data

The paper compares two federated learning setups: parallel training, where clients work simultaneously, and sequential training, where clients update the model one after another. It derives convergence guarantees for the sequential approach when client data distributions differ, a setting where parallel methods often struggle. The analysis aims to clarify when sequential federated learning offers theoretical advantages over its parallel counterpart.

federated-learning/arxiv/optimization/research