williamdevena / Defending-federated-learning-systemView external linksLinks
Implementation of a client reputation, gradient checking and homomorphic encryption mechanism to defend a federated learning system from data/model poisoning and reverse engineering attacks.
☆17Jan 11, 2024Updated 2 years ago
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