{"data":{"id":"e4765a19-ebdc-4eba-8dd8-0e85be8e2ade","title":"Differential Privacy Enabled Cascaded Filter for Efficient and Privacy-Preserving Federated Learning","summary":"Federated learning (FL, a way for multiple computers to train an AI model together without sharing raw data) faces a tradeoff between privacy and performance: encryption methods are slow, while differential privacy (DP, adding noise to data to hide individual information) reduces accuracy. This research proposes a cascaded filter that selectively adds noise only to the most important model parameters (the dimensions with large values and high variation) before sending them to a central server, achieving both privacy protection and better model performance than existing methods.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11653223","publishedAt":"2026-08-12T13:16:39.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"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":"2026-08-12T13:16:39.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}