22 citations · 26 across the 4 of their papers we have counts for
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cs.LG2023★ 22 cited
A Survey on Decentralized Federated Learning
Edoardo Gabrielli, Anthony Di Pietro, Dario Fenoglio +2
Federated learning (FL) enables collaborative training without pooling raw data, but standard FL relies on a central coordinator, which introduces a single point of failure and con…
cs.LG2023★ 4 cited
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
Edoardo Gabrielli, Dimitri Belli, Zoe Matrullo +2
Current defense mechanisms against model poisoning attacks in federated learning (FL) systems have proven effective up to a certain threshold of malicious clients. In this work, we…