A data sharing method for remote medical system based on federated distillation learning and consortium blockchain. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Li, Ning; Zhang, Ruijie; Zhu, Chengyu; Ou, Wei; Han, Wenbao; Zhang, Qionglu
• Is Part Of: Connection science. Volume 35:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Remote medical equipment -- consortium blockchain -- federated learning -- knowledge distillation -- reputation incentive; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2023.2186315
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients' health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks.