{"data":{"id":"03d1cbd9-dac3-4589-9122-949ba81da854","title":"Model Stability Defense Against Model Poisoning in Federated Learning","summary":"This research paper proposes Model Stability Defense for Federated Learning (MSDFL), a method that defends federated learning systems against model poisoning by minimizing the norm of the model-output Jacobian matrix. A refined variant, HMSDFL, extends stability to all logit output dimensions to address convergence-speed disparities caused by MSDFL. The authors report extensive experiments showing fidelity, robustness, compatibility, and self-protection, though the source text gives no numerical results.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11194751","publishedAt":"2025-10-07T13:16:58.000Z","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":null,"fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}