20 citations · 20 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone +1
Federated Learning (FL) has emerged as the state-of-the-art approach for learning from decentralized data in privacy-constrained scenarios.However, system and statistical challenge…
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…