ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA for filtering malicious updates via adaptive thresholds and oracle-based clustering to achieve Byzantine-resilient decentralized learning for EV battery intelligence.
Byzantine-robust dis- tributed learning: Towards optimal statistical rates
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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.
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 for filtering malicious updates via adaptive thresholds and oracle-based clustering to achieve Byzantine-resilient decentralized learning for EV battery intelligence.
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FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
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.
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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.