Federated Learning for Smart Healthcare: A Survey
- URL: http://arxiv.org/abs/2111.08834v1
- Date: Tue, 16 Nov 2021 23:34:22 GMT
- Title: Federated Learning for Smart Healthcare: A Survey
- Authors: Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna
Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang
- Abstract summary: Federated Learning (FL) as an emerging distributed collaborative AI paradigm is particularly attractive for smart healthcare.
We present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare.
We provide a review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection.
- Score: 39.68559637397757
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in communication technologies and Internet-of-Medical-Things
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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