FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning

Neural Information Processing Systems 

To bridge this gap, we model the strategic interactions between the defender and dynamic attackers as a minimax game. Based on the analysis of the game, we design an interactive defense mechanism FedGame. We prove that under mild assumptions, the global model trained with FedGame under backdoor attacks is close to that trained without attacks.

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