Federated Learning and Data Privacy in Connected Healthcare Devices
Abstract
Federated learning has emerged as a transformative paradigm for secure and intelligent healthcare systems, enabling collaborative model training without centralized data aggregation. This book chapter explores the architectural foundations, privacy-preserving techniques, and real-world applications of federated learning in connected healthcare environments. The discussion emphasizes the integration of decentralized artificial intelligence with Internet of Medical Things (IoMT) devices, facilitating clinical decision support, real-time health prediction, and continuous patient monitoring while maintaining strict compliance with data protection regulations. The chapter examines communication frameworks, model update mechanisms, and scalable system architectures that ensure interoperability across diverse healthcare infrastructures. Security challenges such as data poisoning, inference attacks, and model inversion are analyzed in conjunction with robust defense mechanisms including differential privacy, secure multi-party computation, and homomorphic encryption. Through an in-depth examination of federated learning’s role in privacy-preserving analytics, this work highlights its potential to revolutionize precision medicine, telehealth, and patient-centric digital ecosystems. The synthesis of distributed intelligence and ethical AI practices positions federated learning as a cornerstone technology for the future of connected and trustworthy healthcare innovation.
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