Open problems in medical federated learning

Purpose This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how techniques can be applied field are presented. About 80 reference studies described were reviewed, framework currently being developed by research team is provided. paper will help researchers build an actual environment. Design/methodology/approach Since machine emerged, more efficient analysis was possible with a large amount data. However, data regulations have been tightened worldwide, usage centralized methods has become almost infeasible. Federated introduced as solution. Even its powerful structural advantages, there still exist unsolved challenges real those category presents Findings provides four categorized aware when applying technique environment, then general guidelines for building environment Originality/value Existing dealt such heterogeneity problems itself, but lacking on these incur working tasks. Therefore, this helps understand through examples environments.