SecCL: Securing Collaborative Learning Systems via Trusted Bulletin Boards

Abstract

Massive and diverse data is crucial to train a general deep learning model, while the data collection for model training is difficult, especially training on sensitive data (e.g., medical data and face imaging). The emerging collaborative learning addresses this issue well by allowing participants to train a global model by uploading a subset of parameter changes, instead of the entire training data, to a centralized server. However, this privacy-preserving method can effectively enable privacy protection only when the involving entities are trusted (i.e., they honestly follow the protocol). Otherwise, the method may still leak private data. In this article, we propose a secure collaborative learning system named SecCL, which leverages a trusted bulletin board built on blockchain to enable strong privacy protection in collaborative learning by ensuring authentic and correct message interaction during the training process. Also, we develop a novel smart contract for SecCL so that participants can achieve consensus to restrain malicious behaviors. Therefore, SecCL ensures that the server cannot deceive participants and that participants behave well during the training process. We implement a prototype to evaluate its performance, and the promising experimental results demonstrate that SecCL can throttle malicious behaviors of participants and parameter servers while ensuring the accuracy of the global model.