Challenges, Applications and Design Aspects of Federated Learning: A Survey

Federated learning (FL) is a new technology that has been a hot research topic. It enables the training of an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them. There are many application domains in which considerable properly labeled...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.124682-124700
Main Authors: Rahman, K. M. Jawadur, Ahmed, Faisal, Akhter, Nazma, Hasan, Mohammad, Amin, Ruhul, Aziz, Kazi Ehsan, Islam, A. K. M. Muzahidul, Mukta, Md. Saddam Hossain, Islam, A. K. M. Najmul
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer architecture
Data models
Data privacy
data security
decentralized data
Design factors
Distributed databases
distributed processing
Domains
Federated learning
Image analysis
machine learning
Medical imaging
Medical services
New technology
Organizations
Physicians
Privacy
Training
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Summary:Federated learning (FL) is a new technology that has been a hot research topic. It enables the training of an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them. There are many application domains in which considerable properly labeled and complete data are not available in a centralized location (e.g., doctors' diagnoses from medical image analysis). There are also growing concerns over data and user privacy, as artificial intelligence is becoming ubiquitous in new application domains. As such, much research has recently been conducted in several areas within the nascent field of FL. Various surveys on different subtopics exist in the current literature, focusing on specific challenges, design aspects, and application domains. In this paper, we review existing contemporary works in related areas to understand the challenges and topics emphasized by each type of FL survey. Furthermore, we categorize FL research in terms of challenges, design factors, and applications, conducting a holistic review of each and outlining promising research directions.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3111118