1 citations · 2 across the 4 of their papers we have counts for
5 papers
End-to-End Verifiable and Robust Federated Learning
Doryan Lesaignoux, Enrique Mármol Campos, Gabriele Spini +2
Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integrity risks once participants o…
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…
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models
Enrique Mármol Campos, Aurora González Vidal, José Luis Hernández Ramos +1
Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning (ML) models while data sources' privacy is still preserved. However, most of exi…
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
Enrique Mármol Campos, Aurora González Vidal, José Luis Hernández Ramos +1
Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages…
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges
Enrique Mármol Campos, Pablo Fernández Saura, Aurora González-Vidal +4
The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through…