3 papers
cs.LG2025
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
Qinjun Fei, Nuria Rodríguez-Barroso, María Victoria Luzón +2
In cross-silo Federated Learning (FL), client selection is critical to ensure high model performance, yet it remains challenging due to data quality decompensation, budget constrai…
cs.LG2025
Improving -Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distance
Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón +1
The rapid development of artificial intelligence systems has amplified societal concerns regarding their usage, necessitating regulatory frameworks that encompass data privacy. Fed…
cs.CR2024
FLEX: FLEXible Federated Learning Framework
Francisco Herrera, Daniel Jiménez-López, Alberto Argente-Garrido +6
In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and h…