FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
Byzantine-robust dis- tributed learning: Towards optimal statistical rates
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ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA, a hierarchical filtering-and-clustering aggregation protocol that keeps convergence close to FedProx while limiting attack impact below 0.10.
FedEDAuth filters malicious clients in federated learning for counterfeit IC detection by analyzing embedding distributions from a golden reference, achieving 100% detection of poisoned clients and 94.17% model accuracy in tests with 50 participants.
Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.
citing papers explorer
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Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA, a hierarchical filtering-and-clustering aggregation protocol that keeps convergence close to FedProx while limiting attack impact below 0.10.
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FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection
FedEDAuth filters malicious clients in federated learning for counterfeit IC detection by analyzing embedding distributions from a golden reference, achieving 100% detection of poisoned clients and 94.17% model accuracy in tests with 50 participants.
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Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.