Telemedicine data secure sharing scheme based on heterogeneous federated learning

Abstract

Abstract The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient’s medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient’s medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost.

Keywords

• Telemedicine
• Federated learning
• Heterogeneous
• SM9 algorithm
• Threshold signature