most citedSpeeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

20 citations · 20 across the 1 of their papers we have counts for

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1 paper

cs.LG2022★ 20 cited

Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

Riccardo Zaccone, Andrea Rizzardi, Debora Caldarola +2

Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. T…