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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.




Large Language Models as Commonsense Knowledge for Large-Scale Task Planning Anonymous Author(s) Affiliation Address email Appendix 1 A Experimental environments 2 We use the VirtualHome simulator [

Neural Information Processing Systems

A.1 List of objects, containers, surfaces, and rooms in the apartment We list all the objects that are included in our experimental environment. We use the object rearrangement tasks for evaluation. The tasks are randomly sampled from different distributions. Simple: this task is to move one object in the house to the desired location. Novel Simple: this task is to move one object in the house to the desired location.