Blockchain-Based Secure Federated Learning with Incentives: An Incomplete Information Static Game Approach

Federated learning (FL) is a distributed artificial intelligence (AI) paradigm that enables clients to exchange local updates and builds the global AI model on the central server. However, the performance of traditional FL is easily affected by poisoning attacks because the central server cannot check the validity of local updates. Moreover, traditional FL lacks an effective incentive mechanism to sufficiently motivate clients to update local models actively. To address the above problems, we first propose a blockchain-based FL (BFL) framework to defend against the poisoning attack in a decentralized manner while ensuring the high performance of the global model. Then we design an incentive mechanism based on the static game with incomplete information to encourage legitimate nodes to participate in model training and remove attackers from the BFL. Moreover, we find the Nash equilibrium where legitimate nodes can always defend against poisoning attacks and provide high-quality models. Security analysis and simulation results show the security and efficiency of the proposed schemes.