A Blockchain-Based Federated Learning Scheme for Data Sharing in Industrial Internet of Things
Guangxia Xu, Zhaojian Zhou, Jingnan Dong, Lejun Zhang, Xiaoling Song
Abstract
As the Industrial Internet of Things (IIoT) continues to grow in scale, edge devices will generate massive amounts of data every single day. However, most of the IIoT data exists in the form of data silos, which makes it difficult to share data across domains securely. Therefore, a secured data-sharing scheme for IIoT based on blockchain and federated learning (FL) is proposed in this article. Leveraging blockchain in FL systems to enhance the tamper-proof and decentralized capabilities of IIoT devices. Model parameter validation and incentives are also added to the consensus algorithm to encourage more IIoT data owners to contribute local privacy data and arithmetic power. To address potential security issues, such as parameter leakage and inference attacks in data sharing, this article designs an adaptive differential privacy mechanism and a node contribution consensus mechanism. Without affecting the global model’s accuracy, some of the noise is also reduced. The reputation mechanism is used to resist poisoning attacks by malicious nodes. It is demonstrated on different data sets that our scheme has high global model accuracy and can effectively resist 30% model poisoning attacks.