2 papers
cs.LG2025
FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks
Enrique Mármol Campos, Aurora González Vidal, José Luis Hernández Ramos +1
Federated Learning (FL) has become a powerful technique for training Machine Learning (ML) models in a decentralized manner, preserving the privacy of the training datasets involve…
cs.CR2025
On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Daniel M. Jimenez-Gutierrez, Yelizaveta Falkouskaya, Jose L. Hernandez-Ramos +3
Federated Learning (FL) is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While…