{"data":{"id":"28e48b53-7e73-4bd2-b420-132c1ccc40af","title":"AMF-CFL: Anomaly model filtering based on clustering in federated learning","summary":"Federated learning (a system where multiple participants train a shared AI model without sharing their raw data) is vulnerable to attacks from malicious clients who send harmful model updates. This paper proposes AMF-CFL, a defense method that uses multi-k means clustering (a technique for grouping similar data points) and z-score statistical analysis (a way to identify unusual values) to filter out malicious updates and protect the global model, even when clients have non-i.i.d. data distributions (when each participant's data differs significantly in type and quantity).","solution":"AMF-CFL reduces the influence of malicious updates through a two-step filtering strategy: it first applies multi-k means clustering to identify anomalous update patterns, followed by z-score-based statistical analysis to refine the selection of benign updates.","labels":["security","research"],"sourceUrl":"https://www.sciencedirect.com/science/article/pii/S2214212626000177?dgcid=rss_sd_all","publishedAt":"2026-03-16T20:12:19.557Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_poisoning"],"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":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}