Federated Learning for Healthcare: Systematic Review and Architecture Proposal

By Rodolfo Stoffel Antunes, Cristiano André da Costa, Arne Küderle, Imrana Abdullahi Yari, B. Eskofier. ACM Transactions on Intelligent Systems and Technology, 2022

598 citations recorded by the source corpus.

The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/d43e48c5e0fac33df3259d655f3316d43290d763

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Citations in the corpus

Federated learning for medical image analysis: A survey (2023, background): “Several recent surveys introduce the applications of federated learning in medicine and healthcare areas (Antunes et al., 2022; Rajendran et al., 2021; Nguyen et al., 2022; Pfitzner et al., 2021; Rieke et al., 2020).”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “…values indicating the relative strength of the respective FL approach in that domain. metrics, which are the focal points of current state-of-the-art (SOTA) research and worthy of further investigation [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].”