Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms that can effectively work with decentralized data sources (e.g., hospitals) without acquiring any private data. Recently, applications of FL have vastly expanded into multiple domains, particularly the medical do...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 10; p. 4100
Main Authors Hwang, Seong Oun, Majeed, Abdul
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2024
Subjects
AI models
Algorithms
COVID-19
data sharing
Epidemics
federated learning
Geospatial data
Health care industry
Hospitals
Machine learning
medical domain
Pandemics
Personal information
Poisoning
Privacy
privacy preservation
Taxonomy
training data
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Summary:Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms that can effectively work with decentralized data sources (e.g., hospitals) without acquiring any private data. Recently, applications of FL have vastly expanded into multiple domains, particularly the medical domain, and FL is becoming one of the mainstream technologies of the near future. In this study, we provide insights into FL fundamental concepts (e.g., the difference from centralized learning, functions of clients and servers, workflows, and nature of data), architecture and applications in the general medical domain, synergies with emerging technologies, key challenges (medical domain), and potential research prospects. We discuss major taxonomies of the FL systems and enlist technical factors in the FL ecosystem that are the foundation of many adversarial attacks on these systems. We also highlight the promising applications of FL in the medical domain by taking the recent COVID-19 pandemic as an application use case. We highlight potential research and development trajectories to further enhance the persuasiveness of this emerging paradigm from the technical point of view. We aim to concisely present the progress of FL up to the present in the medical domain including COVID-19 and to suggest future research trajectories in this area.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14104100