Federated Learning for Privacy-Preserved Medical Internet of Things

Abstract

Healthcare is one of the notable areas where the integration of the Internet of Things (IoT) is highly adopted, also known as the Medical IoT (MIoT). So far, MIoT is revolutionizing healthcare because it provides many advantages for the benefit of patients and healthcare personnel. The use of MIoT is becoming a booming trend, generating a large amount of IoT data, which requires proper analysis to infer meaningful information. This has led to the rise of deploying artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL) algorithms, to learn the meaning of this underlying medical data, where the learning process usually occurs in the cloud or telemedicine servers. Due to the exponential growth of MIoT devices and widely distributed private MIoT data sets, it is becoming a challenge to use centralized learning AI algorithms for such tasks. In this connection, federated learning (FL) is gaining traction as a possible method of learning on devices that do not need to migrate private and sensitive data to a central cloud. The terminal equipment and the central server in FL only share learning model updates to ensure that sensitive data is always kept secret. Even though this has recently become a promising research area, no other research has been conducted on this topic recently. In this paper, we synthesize recent literature and FL improvements to support FL-driven MIoT applications and services in healthcare. The findings of this research help stakeholders in academia and industry to realize the competitive advantage of the most advanced privacy preserved MIoT systems based on federal learning.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems

“…N.N. Thilakarathne et al [ 39 ] suggested a general strategy for federated learning (FL) as a potential solution to learning about Medical IoT (MIoT) that does not necessitate moving private and sensitive data to a central cloud. In [ 40 ], a predictive approach utilizing the cloud and an IoT-based database is suggested for forecasting the diseases that used the patients’ data collected from biosensors.…”

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

“…On the other hand, with the further development of 5G IoT technology, privacy and security have become critical points for the industry to consider [1]. FL [2] dramatically guarantees data privacy in the model training process [3] and provides an effective solution to the islanding effect in ML. Based on securing the data privacy of industrial devices, FL can incorporate numerous industrial devices into the ML training process, saving communication resources during model training [4] and improving the training convergence rate and learning accuracy [5].…”