Privacy, Robustness, and Fairness Trade-Offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface

Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its deployment in operational environments must simultaneously satisfy three competing requirements: formal differential privacy guaranties, tolerance to...

On the Security and Privacy of Federated Learning: a Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

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 FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provide...

Generalization under Byzantine and Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning

Whitepaper called Generalization Under Byzantine and Poisoning Attacks: Tight Stability Bounds In Robust Distributed Learning...

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation

Federated Learning FL enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, wh...

Coded Robust Aggregation for Distributed Learning under Byzantine Attacks

In this paper, we investigate the problem of distributed learning DL in the presence of Byzantine attacks. For this problem, various robust bounded aggregation RBA rules have been proposed at the central server to mitigate the impact of Byzantine attacks. However, current DL methods apply RBA rul...