Federated Learning for Healthcare Informatics

By Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter B. Walker, Jiang Bian, Fei Wang. 2019

1,462 citations recorded by the source corpus.

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, “big data.” Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.

Subfields: Artificial Intelligence, Health Information Management

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Citations in the corpus

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (2020, background): “Several current applications of FL include text prediction in mobile device messaging [1, 2, 3, 4, 5], speech recognition [6], face recognition for device access [7, 8], and maintaining decentralized predictive models across health organizations [9, 10, 11].”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “The predominant strategies for addressing this issue have involved the reframing of the data to achieve a uniform distribution (consensus solutions) or the embracing of the heterogeneity of the data 69,91,92 (pluralistic solutions).”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “In [16], the authors survey the current progress on federated learning in the domain of healthcare informatics.”
The future of digital health with federated learning (2020, background): “[36], but various other definitions have been proposed in literature [14, 11, 15, 9].”
Federated Learning for Smart Healthcare: A Survey (2021, background): “The work in [17] mainly discusses the FL architectures and models with a very brief introduction to the roles of FL in healthcare informatics.”
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, background): “resulting in a significantly improved performance of the ML model [7].”
From distributed machine learning to federated learning: a survey (2021, background): “, healthcare informatics [188], mobile edge networks [99], and neural architecture searches [205], and they personalize global models to work better for individual clients [87].”
Federated Learning on Non-IID Data: A Survey (2021, background): “Overviews of FL applications to IoT and edge devices [73, 56, 117], wireless networks [53], mobile devices [85], and healthcare [143] have also been reported.”
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework (2020, background): “Moreover, the number of data samples across devices may vary significantly [11].”
Biscotti: A Blockchain System for Private and Secure Federated Learning (2021, background): “However, when training ML models in a multi-party setting, users must share their potentially sensitive information with a centralized service [1], [2], [3], [4].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “The authors of [101] provided a review of FL technologies within the biomedical space and the challenges.”
FedProto: Federated Prototype Learning across Heterogeneous Clients (2021, methodology): “2020) and in related applications, such as healthcare (Rieke et al. 2020; Xu et al. 2020) and street image object detection (Luo et al.”
Decentralized Federated Learning: A Survey and Perspective (2023, methodology): “Although FL has shown unprecedented advantages, most of the current research has been limited to CFL. DFL, as an essential branch in FL, is proliferating and offering beneﬁts over CFL. Recent surveys have focused more on CFL, with less attention given to DFL [61], [75], [76], [77].”
Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “[14] briefly discuss the applications of FL on healthcare applications.”
A Federated Learning Framework for Healthcare IoT devices (2020, positive): “…proposed by Google (Koneˇcn`y et al., 2016; McMahan et al., 2017), is a new attempt to resolve this data dilemma, which is deﬁned as a problem of training a high-quality shared global model with a central server from decentralized data scattered among a large number of clients (Xu & Wang, 2019).”