{"data":{"id":"1dbfa88f-59ef-4b0b-a610-03fc8bdb9360","title":"FairRoP: Robust Client Selection Scheme for Fairness-Aware Federated Learning","summary":"The paper proposes FairRoP, a client selection scheme for federated learning that aims to improve fairness and robustness at the same time. It models the problem as a multi-objective optimization solved with an ε-greedy Thompson Sampling Multi-Armed Bandit, using fairness awareness, attack detection and q-Balance submodules. Experiments on real datasets reportedly show significant gains in fairness and robustness over state-of-the-art methods, and the approach integrates with other aggregation algorithms.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11534500","publishedAt":"2026-05-22T13:16:59.000Z","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":"2026-05-22T13:16:59.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}