PEFL: Privacy-Preserved and Efficient Federated Learning With Blockchain
Lei Tian, Feilong Lin, Jiahao Gan, Riheng Jia, Zheng Zheng, Minglu Li
Abstract
With the rise of federated learning (FL) in the realm of machine learning for data privacy protection, its unique distributed data processing characteristics have garnered widespread attention. However, the implementation of FL faces many challenges, as achieving a balance between data privacy, model security, and system efficiency is difficult, often requiring the sacrifice of efficiency for privacy and security. Moreover, this process typically assumes the existence of a trusted server for coordination. Addressing these challenges, this article proposes a privacy-preserved and efficient FL framework with blockchain (PEFL). PEFL utilizes blockchain and differential privacy techniques to coordinate privacy protection among clients, and filters out anomalous model parameters through an aggregation-side detection algorithm to resist poisoning attacks. Under the assumption of an untrusted server, we design the model-validated fault-tolerant federation (MFF) consensus mechanism based on a committee, balancing efficiency expectations to regulate the server and ensure the reliability of the training process. Through experiments on the MNIST and CIFAR10 datasets, and comparison with typical FL schemes, PEFL demonstrates better defense against various attack models. Besides, it achieves higher training efficiency while ensuring privacy security.