{"data":{"id":"747faa73-6745-4f66-b0cb-194bbb5d87f7","title":"Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning","summary":"This paper proposes two federated learning frameworks, DRAG and BR-DRAG, to counter client drift from data heterogeneity and Byzantine attacks. DRAG uses a reference direction and a divergence-of-degree metric to linearly calibrate each worker's local update without extra communication. BR-DRAG extends this under Byzantine attacks by keeping a vetted root dataset at the server to produce trusted reference directions, and the authors report analytical convergence proofs for non-convex models plus experiments showing superior performance over state-of-the-art methods.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11579423","publishedAt":"2026-06-25T13:17:25.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["other"],"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":"2026-06-25T13:17:25.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}