Federated Learning and Collaborative AI in Medical Diagnostics: A Conceptual and Literature-Based Study

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Author(s): Shaista Ashraf Farooqi (Asia e University, Pakistan)
Copyright: 2027
Pages: 40
Source title: Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), George Pappas (Lawrence Technological University, USA)and Sandeep Thota (Oracle Inc., USA)
DOI: 10.4018/979-8-3373-7426-0.ch008

Keywords: Health Information Systems / Information Security & Privacy / Medical Information Science Reference / Security & Forensics

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Abstract

Advancements in Federated Learning (FL) and collaborative Artificial Intelligence (AI) are reshaping medical diagnostics by enabling hospitals and research institutions to build powerful models without centralized data sharing, as patient information remains on local servers. This section outlines the core principles of FL and the rise of trustworthy, privacy-preserving collaborative AI systems across healthcare networks. It reviews prior work, key techniques, and system designs, explaining how they enhance diagnostic accuracy, efficiency, and personalization. The discussion also highlights real-world applications, emerging trends, and challenges such as interoperability, regulatory compliance, and computational demands. The chapter informs scholars, practitioners, and policymakers on how FL and collaborative AI can transform medical diagnostics and support secure, ethical, and innovative healthcare.

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Federated Learning and Collaborative AI in Medical Diagnostics: A Conceptual and Literature-Based Study

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