Federated learning with blockchain and IOT for secure and privacy-preserving healthcare systems
Abstract
The increasing use of Internet of Things (IoT) technologies in healthcare has enabled continuous patient monitoring, early disease detection, and intelligent clinical decision-making. Despite these benefits, healthcare systems face serious challenges related to data privacy, security, and trust due to the highly sensitive nature of medical information. Traditional centralized machine learning approaches require patient data to be transferred and stored on central servers, making them vulnerable to data breaches, cyberattacks, and unauthorized access.Federated learning has emerged as a privacy-preserving machine learning paradigm that allows multiple healthcare institutions to collaboratively train models without sharing raw patient data. However, federated learning alone does not sufficiently address issues related to trust, transparency, and malicious participants. Blockchain technology offers a decentralized and tamper-resistant solution by ensuring secure coordination, immutability, and accountability among participants. When combined with IoT-based healthcare systems, blockchain-enabled federated learning provides a robust framework for secure and privacy-preserving medical data analytics.This chapter presents an integrated architecture that combines federated learning, blockchain, and IoT to enhance cybersecurity and data privacy in healthcare systems. The proposed framework ensures data confidentiality by keeping patient information localized while using blockchain and…
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