{"data":{"id":"05f3ed6f-eeec-4a9e-95d4-58ef1ea4d0e0","title":"PPFPL: Cross-Silo Privacy-Preserving Federated Prototype Learning Against Data Poisoning Attacks","summary":"This research article proposes PPFPL, a privacy-preserving federated prototype learning framework for cross-silo federated learning. It uses prototypes as client-submitted model updates to limit the effect of poisoned Non-IID data, and a secure aggregation protocol built on homomorphic encryption that runs on two servers to achieve Byzantine-robust aggregation. The authors report theoretical convergence and privacy analyses and experiments on public datasets showing resistance to data poisoning under Non-IID settings.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11298519","publishedAt":"2025-12-12T13:17:24.000Z","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":"2025-12-12T13:17:24.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}