{"data":{"id":"cfa30a58-b100-4c53-8f60-41c124d24065","title":"AMBER: Robust Federated Learning Based on Client Verification","summary":"This paper analyzes Local Chained Attacks (LCAs), which span local dataset pollution, selective inputs, and training process tampering in federated learning clients. It proposes AMBER, a three-layer verification framework that uses vector commitments for dataset integrity, a local consistency check for selective input attacks, and TEE-based verification of input-output correlation. Experiments across multiple datasets, models, and attack scenarios report better defense effectiveness than existing approaches with low overhead, including in Non-IID settings.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11580359","publishedAt":"2026-06-26T13:17:17.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_poisoning"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-06-26T13:17:17.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}