Secure Feedback to Edge Servers in Distributed Machine Learning Using Rich Clients
Saki Takano, Akihiro Nakao, Saneyasu Yamagchi, Masato Oguchi · 2023
The use of data collected by edge devices in machine learning, including personal information, has become an important trend in recent years. Most distributed machine learning methods such as Federated Learning aggregate and manage all data or training results on a high-performance edge servers. However, passing users’ personal information to an external server may involve some privacy concerns owing to the risk of information leakage. To address this problem, we consider a distributed machine learning model with excellent privacy protection in which the user can choose not to pass any personal data to 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 a Jetson Nano as an edge device. The experimental results confirm that edge devices were able to use personal information in a short period of time, while the edge server was able to obtain more accurate results by integrating several training results. Thus, the results show that the proposed model enables the safe and efficient utilization of data collected by edge devices.