Federated Learning for Healthcare: Systematic Review and Architecture Proposal

Abstract

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.

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“…Thus, further external validation of the AI models in a diverse hospital is warranted. Moreover, future studies should encourage more hospitals to cooperate so that the study’s generalizability can be extended through federated learning [ 22 ] or other methods of merging more hospitals’ big data to improve the quality and stability of the AI models.…”

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“…Nevertheless, any transmission of information automatically implies the possibilities of privacy and security breaches. Hence, considerable research is ongoing to ensure data security and privacy in federated learning, with many different techniques being proposed, such as homomorphic encryption and differential privacy [119], [120].…”

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“…Prior proposed works to overcome this problem include federated learning techniques. For instance, the studies [9][10][11] reviewed the current applications and technical considerations of the federated learning technique to preserve the sensitive biomedical data. The impact of federated learning is examined through the stakeholders, such as patients, clinicians, healthcare facilities, and manufacturers.…”