SecCL: Securing Collaborative Learning Systems via Trusted Bulletin Boards 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 privacypreserving 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.

Medical Image Collaborative Training Based on MultiBlockchain In this paper, we introduce a novel privacypreserving decentralized collaborative training approach for medical image analysis, based on a multiBlockchain system. With this approach, researchers from different medical institutions can collaboratively train machine learning models without sharing sensitive patient data. A partialparameter propagation approach is applied to prevent indirect patient information leakage during model propagation. Based on peertopeer communication in a multiBlockchain system, one training task can benefit from other tasks in other Blockchains, according to their task similarity. Furthermore, a personalized model for each institution is learned after collaborative training. Compared with training models by gathering all participantsu0027 datasets, experiment results show that our approach achieves similar performance while protecting sensitive patient data. Transferring the trained model from other Blockchains is also proved to be effective in accelerating model convergence. The personalized model process further improves model accuracy.