Edge intelligent collaborative privacy protection solution for smart medical

Abstract

In the era of big data, competent medical care has entered people’s lives. However, the existing intelligent diagnosis models have low accuracy and poor universality. At the same time, there is a risk of privacy leakage in the process of health monitoring and auxiliary diagnosis. This paper combines edge computing and federated learning ensure model accuracy and protect patient privacy by proposing an Edge intelligent collaborative privacy protection solution for smart medical (EICPP). First, we offer a lightweight edge intellectual collaborative federated learning framework named KubeFL to support health monitoring and auxiliary diagnosis; secondly, we design a federated learning training model based on device-edge-cloud layering, with complete accuracy of up to 95.8%; Finally, a differential privacy algorithm for edge-cloud model transmission is proposed, which can exchange a lower accuracy loss for solid privacy protection.