Federated Learning in Healthcare
Abstract
Machine learning is steadily becoming into a useful technology that supports research and discovery across a variety of fields, including healthcare. For machine learning models to be effective, there must be vast amounts of objective, varied, and easily accessible data.
However, too frequently, due to privacy concerns, datasets are restricted to silos inside their various healthcare entities, limiting important potential insights from being realized through collaboration. The potential of exchanging data for machine learning in the healthcare sector is complicated by strict patient privacy laws. In the field of intelligent healthcare, explainable artificial intelligence (XAI), artificial intelligence (Al), and federated learning (FL) are the most popular and interesting techniques. In the past, the healthcare system functioned on the idea of centralized agents sharing their unprocessed information. As a result, this system still has plenty of limitations and issues. The system would instead comprise of a number of agent collaborators with Al that are capable of communicating with their desired host. Another interesting feature is FL, which operates decentralized and keeps communication according to a model in the selected system without sharing raw data. Many limitations and challenges facing the medical sector may be reduced by a blend of FL, Al, and XAI strategies.
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References
O. Young, "Synthetic structure of industrial plastics (Book style with paper title and editor)," in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15—64.
W. K. Chen, Linear Networks and Systems (Book style) Belmont, CA: Wadsworth, 1993, pp. 123—135. Poor, An Introduction to Signal Detection and Estimation. New York' Springer-Verlag, 1985, ch. 4.
B. Smith, "An approach to graphs of linear forms (Unpublished work style)," unpublished.
E. H. Miller, "A note on reflector arrays (Periodical style—-Accepted for publication)," IEEE Trans Antennas Propagat., to be published.
J _ Wang, "Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication)," IEEE J Quantum Electron., submitted for publication.
