ClusterGuard: Secure Clustered Aggregation for Federated Learning With Robustness

Federated learning (a system where multiple parties train AI models together while keeping their data private) faces two main problems: model updates can leak sensitive information, and it's hard to detect poisoning attacks (when malicious participants deliberately corrupt the training process). ClusterGuard is a new secure aggregation protocol (a method for safely combining model updates from many participants) that uses clustering, masking techniques, and filtering mechanisms to protect privacy while detecting and resisting poisoning attacks, even when up to 20% of participants are malicious.