Towards efficient federated learning‐based scheme in medical cyber‐physical systems for distributed data. (29th September 2020) - Guo, Kehua; Li, Nan; Kang, Jian; Zhang, Jian
Summary: In recent years, Cyber‐Physical Systems (CPS) and Artificial Intelligence (AI) have made good progress in the medical field. The medical CPS (MCPS) based on AI can realize the efficient and reasonable utilization of medical resources and improve the quality of medical process. However, current MCPS are still facing several challenges, and the privacy protection of medical data is one of the most critical challenges. Since medical data is stored in different hospitals, most studies collect data from decentralized hospitals to train a disease diagnosis model, which is not conducive to the privacy protection of patients. And in some existing solutions, it is also difficult for doctors to select the optimal model from multiple models in clinical diagnosis. In this paper, we propose a novel scheme based on federated learning in MCPS for training disease diagnosis models from distributed medical image data. Our scheme is divided into three parts: the model provider, the server, and the consumer, and a detailed working process is designed for each part. This scheme can not only effectively solve the problem of privacy protection, but also solve the problem of model selection for doctors and save storage space. It can ensure that consumers automatically get a steadily improved disease diagnosis model. This scheme is performed on simulated distributed medical image datasets. The experimental results show the effectiveness and superiority of our scheme.