NttpFL: Privacy-Preserving Oriented No Trusted Third Party Federated Learning System Based on Blockchain
Shuangjie Bai, Geng Yang, Guoxiu Liu, Hua Dai, Chunming Rong
Abstract
In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuitable for federated learning and vulnerable to security risks. To mitigate these issues, we propose a privacy-preserving oriented no trusted third party federated learning system based on blockchain (NttpFL). The initiator of the federated learning task and the partners negotiate keys through the conference key agreement and do not need to distribute keys through a trusted third party. We design a double-layer encryption mechanism to ensure privacy. Partners cannot obtain any private information other than their information. The decentralized nature of blockchain suits our system. In addition, blockchain makes the entire process transparent and traceable and avoids the single node failure problem. Experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.