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Challenges, Applications and Design Aspects of Federated Learning: A Survey - 2021

Challenges, Applications And Design Aspects Of Federated Learning: A Survey

Research Area:  Machine Learning


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.


Author(s) Name:  K. M. Jawadur Rahman; Faisal Ahmed; Nazma Akhter; Mohammad Hasan; Ruhul Amin; Kazi Ehsan Aziz; A. K. M. Muzahidul Islam; Md. Saddam Hossain Mukta; A. K. M. Najmul Islam

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/ACCESS.2021.3111118

Volume Information:  Volume: 9,Page(s): 124682 - 124700