Artificial Identification: A Novel Privacy Framework for Federated Learning Based on Blockchain
Liwei Ouyang, Fei–Yue Wang, Yonglin Tian, Xiaofeng Jia, Hongwei Qi, Ge Wang
Abstract
To provide off-chain federations with complete privacy services to realize on-chain federated learning (FL), this article proposes a novel privacy framework for FL based on blockchain and smart contracts, named Artificial Identification. It consists of two modules: private peer-to-peer identification and private FL, using two scalable smart contracts to manage the identification and learning process, respectively. Based on Ethereum and interplenary file systems (IPFS), we implement our framework and comprehensively analyze its performance. Experiments show that the proposed framework has acceptable collaboration costs and offers advantages in terms of privacy, security, and decentralization. Furthermore, combined with radio frequency identification (RFID) technology, the framework has the potential to realize automatic on-chain identification and autonomous FL of machine clusters composed of Internet of Things (IoT) devices or distributed participants.