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Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
Sustainable Communication Technologies, SINTEF Digital, Trondheim, Norway.ORCID iD: 0000-0002-2142-9522
Department of Electrical Engineering and Computer Science, DoD Center of Excellence in Artificial Intelligence and Machine Learning, College of Engineering and Architecture, Howard University, Washington, DC, USA.ORCID iD: 0000-0002-2014-2749
Department of Radiology, Machine and Hybrid Intelligence Lab, Northwestern University, Chicago, IL, USA.ORCID iD: 0000-0002-8078-6730
Sustainable Communication Technologies, SINTEF Digital, Trondheim, Norway.ORCID iD: 0000-0003-4329-1410
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2024 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 11, no 5, p. 7374-7398Article in journal (Refereed) Published
Abstract [en]

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. Vol. 11, no 5, p. 7374-7398
Keywords [en]
Medical services;Medical diagnostic imaging;Biomedical equipment;Data privacy;Surveys;Internet of Things;Cancer;Artificial intelligence (AI);communication;data privacy;edge computing;federated learning (FL);foundational model (FMs);large language model (LLM);medical applications;security
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Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-345592DOI: 10.1109/JIOT.2023.3329061Scopus ID: 2-s2.0-85181574699OAI: oai:DiVA.org:kth-345592DiVA, id: diva2:1851207
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QC 20240415

Available from: 2024-04-12 Created: 2024-04-12 Last updated: 2024-04-15Bibliographically approved

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Vlassov, Vladimir

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