Privacy Protection Federated Learning Framework Based on Blockchain and Committee Consensus in IoT Devices

【Author】 Zhang, Shuxin; Zhu, Jinghua

【Source】2023 IEEE 47TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC

【影响因子】

【Abstract】With the staggering growth of data on modern IoT devices, privacy concerns on IoT networks are becoming increasingly prominent. Thus, federated learning emerged. It trains the model by enabling multiple participants to employ data from multiple parties, making data accessible but not visible. However, user data may encounter issues with data privacy leakage in the existing federated learning environment. Thus, this article suggests BFLPP, a privacy protection federated learning framework based on blockchain and committee consensus in IoT devices. It leverages blockchain to verify local updates, rather than a centralized server and uses it to generate and store global models. It also implements local differential privacy to further secure data privacy. In this paper, we use committee nodes to validate the model parameters, and if we receive enough replies during the validation process. It will submit and upload the validated updates to the update block, then trigger the smart contract to aggregate updates and broadcast it to the nodes for the next training round. Eventually, this paper employs the BFLPP framework on convolutional neural networks to conduct experiments on the MNIST dataset, grounded on blockchain and federated learning. The experimental results indicate the security and effectiveness of the BFLPP framework.

【Keywords】Federated Learning; Privacy Preserving; Blockchain; Internet of Things

【发表时间】2023

【收录时间】2023-10-15

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习