BlockDL: Privacy-Preserving and Crowd-Sourced Deep Learning Through Blockchain

Abstract

Deep learning has become a key technology on modeling large amounts of multi-sourced data. For privacy concerns, the data sharing among companies and organizations is increasingly difficult. In this paper, we present a crowd-sourced federated learning solution to train neural networks with a hybrid blockchain architecture. Smart contracts are used to share data authentications on the main chain, where the proxy re-encryption is for the privacy preserving. A consensus-based asynchronous practical byzantine federated learning (APBFL) algorithm is proposed on the side chains, to improve the model reliability and security. Experiments show that our solution is efficient, secure and robust.