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Defending-federated-learning-system

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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.

Creat2023-04-08T02:06:34
Update2025-03-10T20:32:42
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