Secure and Efficient Federated Learning Schemes for Healthcare Systems

Abstract

The swift advancement in communication technology alongside the rise of the Medical Internet of Things (IoT) has spurred the extensive adoption of diverse sensor-driven healthcare and monitoring systems. While the rapid development of healthcare systems is underway, concerns about the privacy leakage of medical data have also attracted attention. Federated learning plays a certain protective role in data, but studies have shown that gradient transmission under federated learning environments still leads to privacy leakage. Therefore, we proposed secure and efficient federated learning schemes for smart healthcare systems. In this scheme, we used Paillier encryption technology to encrypt the shared training models on the client side, ensuring the security and privacy of the training models. Meanwhile, we designed a zero-knowledge identity authentication module to verify the authenticity of clients participating in the training process. Second, we designed a gradient filtering compression algorithm to eliminate locally updated gradients that were irrelevant to the convergence trend and used computationally negligible compression operators to quantize updates, thereby improving communication efficiency while ensuring model accuracy. The experimental results demonstrated that the proposed scheme not only had high model accuracy but also had significant advantages in communication overhead compared with existing schemes.