Privacy-preserving Decentralized Federated Deep Learning

Abstract

Deep learning has achieved the high-accuracy of state-of-the-art algorithms in long-standing AI tasks. Due to the obvious privacy issues of deep learning, Google proposes Federal Deep Learning (FDL), in which distributed participants only upload local gradients and and a centralized server updates parameters based on the collected gradients. But few users are willing to participate in federated learning due to the lack of contribution evaluation and reward mechanisms. So a decentralized federated deep learning, called DFDL, has been proposed by introducing blockchain to form an effective incentive mechanism for participants. However, DFDL still faces serious privacy issues as blockchain does not guarantee the privacy of training data and model. In this paper, in order to address the aforementioned issues, we propose a new Privacy-preserving DFDL scheme, called PDFDL. With PDFDL, parties can securely learn a global model with their local gradients in the assistance of blockchain, and the parties’ sensitive data and the global model are well protected. Specifically, with a secure multi-party aggregation computing, all local gradients are encrypted by their owners before being sent to the smart contract, and can be directly aggregated without decryption. Detailed security analysis shows that PDFDL can resist various known security threats. Moreover, we give an implementation prototype by integrating deep learning module with a Blockchain development platform (Ethereum V1.6.4). We demonstrate the encryption performance and the training accuracy of our PDFDL on benchmark datasets.