{"data":{"id":"a7f956ab-806f-478b-82e1-9935d41460e4","title":"ClusterGuard: Secure Clustered Aggregation for Federated Learning With Robustness","summary":"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.","solution":"The source proposes ClusterGuard as the solution, which includes: (1) Verifiable Random Function (VRF, a method to ensure fair and transparent grouping of participants) for client clustering, (2) key-homomorphic masking combined with verifiable secret sharing for secure aggregation within clusters, and (3) a dual filtering mechanism based on cosine similarity and norm to detect and resist poisoning attacks. The text notes that ClusterGuard provides two variants for both client-server and decentralized blockchain environments.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11424017","publishedAt":"2026-03-06T13:18:44.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_poisoning","data_extraction"],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-03-06T13:18:44.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality","integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}