survey

Federated Learning for Smart Healthcare: A Survey

Authors:

Dinh C. Nguyen,

Quoc-Viet Pham,

Pubudu N. Pathirana,

Aruna Seneviratne,

Won-Joo HwangAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 3

Article No.: 60, Pages 1 - 37

https://doi.org/10.1145/3501296

Published: 03 February 2022 Publication History

Abstract

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 3

March 2023

772 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3514180

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 03 February 2022

Accepted: 01 November 2021

Revised: 01 August 2021

Received: 01 April 2021

Published in CSUR Volume 55, Issue 3

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National Research Foundation of Korea (NRF)
Korean Government (MSIT)
Institute of Information & communications Technology Planning & Evaluation (IITP)
Artificial Intelligence Convergence Research Center [Pusan National University]
MSIT (Ministry of Science and ICT), Korea
Grand Information Technology Research Center support program
IITP (Institute for Information & communications Technology Planning & Evaluation)
Natural Sciences and Engineering Research Council of Canada (NSERC)

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