A multi-phase randomized mixing-matrix schedule reduces worst-case per-node energy in decentralized federated learning, with convergence analysis for time-varying communication topologies.
Communication-efficient federated learning for heteroge neous clients,
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Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning
A multi-phase randomized mixing-matrix schedule reduces worst-case per-node energy in decentralized federated learning, with convergence analysis for time-varying communication topologies.