most citedProtecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection

4 citations · 4 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CL2025

When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets

Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi +2

Large language models (LLMs) are increasingly exposed to data contamination, i.e., performance gains driven by prior exposure of test datasets rather than generalization. However,…

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…