A training-integrity privacy-preserving federated learning scheme with trusted execution environment

Machine learning models trained on sensitive real-world data promise improvements to everything from medical screening to disease outbreak discovery. In many application domains, learning participants would benefit from pooling their private datasets, training precise machine learning models on the aggregate data, and sharing the profits of using these models. Considering privacy and security concerns often prevent participants from contributing sensitive data for training, researchers proposed several techniques to achieve data privacy in federated learning systems. However, such techniques are susceptible to causative attacks, whereby malicious participants can inject false training results with the aim of corrupting the well-learned model. To end this, in this paper, we propose a new privacy-preserving federated learning scheme that guarantees the integrity of deep learning processes. Based on the Trusted Execution Environment (TEE), we design a training-integrity protocol for this scheme, in which causative attacks can be detected. Thus, each participant is compelled to execute the privacy-preserving learning algorithm of the scheme correctly. We evaluate the performance of our scheme by prototype implementations. The experimental result shows that the scheme is training-integrity and practical.