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.
Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
2 Pith papers cite this work. Polarity classification is still indexing.
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A bilinear hidden Markov model enables privacy-preserving frequency regulation and flexibility estimation for aggregated EVs using only aggregate data.
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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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Data-Driven Privacy-Preserving Modeling and Frequency Regulation with Aggregated Electric Vehicles via Bilinear Hidden Markov Model
A bilinear hidden Markov model enables privacy-preserving frequency regulation and flexibility estimation for aggregated EVs using only aggregate data.