ESB-FL: Efficient and Secure Blockchain-Based Federated Learning with Fair Payment

Federated learning is a technique that enables multiple parties to collaboratively train a model without sharing raw private data, and it is ideal for smart healthcare. However, it raises new privacy concerns due to the risk of privacy-sensitive medical data leakage. It is not until recently that the privacy-preserving FL (PPFL) has been introduced as a solution to ensure the privacy of training processes. Unfortunately, most existing PPFL schemes are highly dependent on complex cryptographic mechanisms or fail to guarantee the accuracy of training models. Besides, there has been little research on the fairness of the payment procedure in the PPFL with incentive mechanisms. To address the above concerns, we first construct an efficient non-interactive designated decryptor function encryption (NDD-FE) scheme to protect the privacy of training data while maintaining high communication performance. We then propose a blockchain-based PPFL framework with fair payment for medical image detection, namely ESB-FL, by combining the NDD-FE and an elaborately designed blockchain. ESB-FL not only inherits the characteristics of the NDD-FE scheme, but it also ensures the interests of each participant. We finally conduct extensive security analysis and experiments to show that our new framework has enhanced security, good accuracy, and high efficiency.