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InfoResearchPeer-reviewed
Topology-Optimal Multiple Gossip Steps for Decentralized Federated Learning via Gossip Tensor
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Summary
This article examines how the topology of a decentralized graph affects the convergence rate of decentralized federated learning (DFL) algorithms. It introduces a tensor-based multiple-gossip-steps method, T-MGS, which uses gossip tensors to guide information flow between sites and minimizes the second-largest absolute eigenvalue of the equivalent gossip matrix. Experiments on simulated and real datasets show T-MGS reduces communication rounds without compromising model accuracy.