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

FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning. Jia, J., Yuan, Z., Sahabandu, D., Niu, L., Rajabi, A., Bhaskar, R., Li, B., & Poovendran, R. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.
bibtex

@inproceedings{Jia2023Fedgame,
	author = {Jia, Jinyuan and Yuan, Zhuowen and Sahabandu, Dinuka and Niu, Luyao and Rajabi, Arezoo and Ramasubramanian Bhaskar and Li, Bo and Poovendran, Radha},
	title = {Fed{G}ame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning} ,
	booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)},
	year = {2023}
}

Downloads: 0