Privacy-protective Distributed Machine Learning between Rich Devices and Edge Servers
Saki Takano, Akihiro Nakao, Saneyasu Yamaguch, Masato Oguchi · Journal of Information Processing · 2024
The utilization of data collected by edge devices, including personal information, in machine learning has emerged as a significant trend in recent years. In most distributed machine learning methods, like Federated Learning, data or training results are typically aggregated and managed on high-performance servers. However, transferring users' personal information to an external server can raise privacy concerns due to the potential risk of data leakage. To tackle this issue, we propose a distributed machine learning model that offers robust privacy protection, allowing users to decide whether or not to share personal data with the server. In the proposed model, the edge device takes over the training at the edge server and sends only the results for which the user has given permission to the edge server for integration. To validate the effectiveness of the proposed model, we performed experiments on facial image recognition using Jetson Nano as an edge device. The experimental results confirmed that edge devices were capable of utilizing personal information in a short time, while the edge server achieved enhanced accuracy by integrating multiple training results. Thus, the results show that the proposed model enables the safe and efficient utilization of data collected by edge devices.