Title: FLGAME: A Game-theoretic Defense against Backdoor Attacks In Federated Learning. Abstract: Federated learning enables the distributed training paradigm, where multiple local clients jointly train a global model without needing to share their local training data. However, recent studies have shown that federated learning provides an additional surface for backdoor attacks. For instance, an attacker can compromise a subset of clients and thus corrupt the global model to incorrectly predict an attacker-chosen target class given any input embedded with the backdoor trigger. Existing defenses for federated learning against backdoor attacks usually detect and exclude the corrupted information from the compromised clients based on a $\textit{static}$ attacker model. Such defenses, however, are less effective when faced with $\textit{dynamic}$ attackers who can strategically adapt their attack strategies. In this work, we model the strategic interaction between the (global) defender and attacker as a minimax game. Based on the analysis of our model, we design an interactive defense mechanism that we call FLGAME. Theoretically, we prove that under mild assumptions, the global model trained with FLGAME under backdoor attacks is close to that trained without attacks. Empirically, we perform extensive evaluations on benchmark datasets and compare FLGAME with multiple state-of-the-art baselines. Our experimental results show that FLGAME can effectively defend against strategic attackers and achieves significantly higher robustness than baselines. 