Federated Learning and Blockchain-Enabled Fog-IoT Platform for Wearables in Predictive Healthcare

• Published in: IEEE transactions on computational social systems Vol. 10; no. 4; pp. 1732 - 1741
• Main Authors: Baucas, Marc Jayson, Spachos, Petros, Plataniotis, Konstantinos N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blockchain; Cryptography; Cybersecurity; Data privacy; distributed systems; Federated learning; fog network; Health care; health care services; health informatics; Health services; Integrity; Internet of Things; Internet of Things (IoT); machine learning; Medical services; platforms; predictive models; Privacy; private blockchain; scalability; Security; Servers; testbed; Wearable computers; Wearable technology
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2023.3235950

Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network's distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform's ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient's privacy and a predictive service's integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.