{"data":{"id":"cfa30a58-b100-4c53-8f60-41c124d24065","title":"AMBER: Robust Federated Learning Based on Client Verification","summary":"Federated learning (FL, a technique where AI models are trained across multiple computers without sending raw data to a central server) is vulnerable to attacks where dishonest participants send corrupted model updates that poison the final model without being detected. This paper introduces AMBER, a framework that adds three layers of verification to check whether clients are trustworthy: confirming data hasn't been tampered with, detecting when clients provide misleading inputs, and verifying that model computations are correct using a trusted execution environment (TEE, a secure area of a computer processor that protects sensitive operations).","solution":"AMBER implements a three-layer verification mechanism: the first layer uses vector commitments to verify dataset integrity and distribution; the second layer employs local consistency-based verification to detect selective input attacks; the third layer enforces computational integrity by verifying the correlation between model inputs and outputs using secure primitives in a Trusted Execution Environment (TEE).","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","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":"2026-06-26T13:17:17.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}