Trusted federated learning for IoMT - solutions and challenges

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


The clinical adoption of state-of-the-art artificial intelligence (AI) applications for disease diagnosis with digital healthcare technologies will ease the burden on medical practitioners, ensure timely interventions, and aid patients’ welfare. The digital innovations in medical data acquisition have made it possible to collect multi-modal data through Internet-of-Medical-Things (IoMT) but the privacy preserving nature and compliance to medical data regulations often precludes data sharing across organizational and geographical boundaries.
An AI model requires high quality and large-scale training datasets collected from diverse sources to mitigate bias and aid better prediction accuracy on the unseen data. With secure and trusted federated learning, the data of an organization can stay local and yet contribute to AI models’ training for building better trained models with improved accuracy and generalization. Thus, the trusted federated learning approaches facilitate sharing the trained AI models across different participating healthcare organizations by breaking down barriers, increasing trust, and preserving privacy for better disease predictions and diagnoses that can increase deployment of AI models in clinical practice. In this chapter, we provide the current state-of-the-art solutions, adoption challenges and future research directions, and a framework for trusted federated learning in healthcare applications.
Original languageEnglish
Title of host publicationFederated Learning for Internet of Medical Things: Concepts, Paradigms, and Solutions
PublisherCRC Press
ISBN (Print)9781032300764
Publication statusPublished - 15 Jun 2023


  • Artificial Intelligence, Blockchain, Edge Computing, Data Privacy, Federated Learning, Messaging Protocols, Bias Mitigation, Healthcare 4.0, Data Science, Secure Machine Learning


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