Flexible and Privacy-preserving Framework for Decentralized Collaborative Learning

Nowadays, collaborative learning is becoming a new trend to address the data scarcity issue. To prevent potential privacy leakage, some privacy-preservation collaborative learning schemes have been proposed with data encryption, but cannot handle the setting of different data contribution among data nodes and avoid the huge overhead of implementing over encrypted data. In this paper, we design a flexible and secure decentralized collaborative learning to achieve the contribution over data nodes, where each data node can specialize the contribution extent for collaborative learning. Besides, we provide a MPC-friendly collaborative layer for the lightweight privacy preservation. Our security analysis and experimental results demonstrate the security and superiority of our system, respectively.