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Federated Learning in a Medical Context: A Systematic Literature Review

Published:02 June 2021Publication History
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Abstract

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

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  1. Federated Learning in a Medical Context: A Systematic Literature Review

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              cover image ACM Transactions on Internet Technology
              ACM Transactions on Internet Technology  Volume 21, Issue 2
              June 2021
              599 pages
              ISSN:1533-5399
              EISSN:1557-6051
              DOI:10.1145/3453144
              • Editor:
              • Ling Liu
              Issue’s Table of Contents

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 2 June 2021
              • Revised: 1 July 2020
              • Accepted: 1 July 2020
              • Received: 1 February 2020
              Published in toit Volume 21, Issue 2

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