Exploiting Unintended Property Leakage in Blockchain-Assisted Federated Learning for Intelligent Edge Computing

Federated learning (FL) serves as an enabling technology for intelligent edge computing, where high-quality machine learning (ML) models are collaboratively trained over large amounts of data generated by various Internet of Things devices while preserving data privacy. To further provide data confidentiality, computation auditability, and participant incentives, the blockchain framework has been incorporated into FL. However, it is an open question whether the model updates from participants in blockchain-assisted FL can disclose properties of the private data the participants are unintended to share. In this article, we propose a novel property inference attack that exploits the unintended property leakage in blockchain-assisted FL for intelligent edge computing. More specifically, we present an active attack to learn the property leakage from model updates of participants and to identify a set of participants with a certain property. We also design a dynamic participant selection strategy tailored to the setting of large-scale FL, which accelerates the selection process of target participants and improves attack accuracy. We evaluate the proposed attack through extensive experiments with publicly available data sets. The experimental results demonstrate that the proposed attack is effective and efficient in inferring various properties of training data, while maintaining the high quality of the main tasks in FL.