{"data":{"id":"c2a610cb-c8b9-4fca-83b6-ffd35ef86862","title":"VFEFL: Privacy-preserving federated learning against malicious clients via verifiable functional encryption","summary":"The paper proposes VFEFL, a privacy-preserving federated learning scheme that encrypts local models with a verifiable functional encryption scheme. It aims to protect local model privacy and defend against malicious clients who upload poisoned models or wrong ciphertexts or decryption keys, within the basic one-server, multiple-client architecture. The authors report that prototype experiments show it resists targeted and untargeted poisoning attacks while preserving model privacy.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy","research"],"sourceUrl":"https://www.sciencedirect.com/science/article/pii/S2214212626000451?dgcid=rss_sd_all","publishedAt":"2026-03-16T20:12:19.544Z","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":["confidentiality","integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}