A2 Review article, literature review, systematic review

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


Open Access publication


Publication Details

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

Publisher: Institute of Electrical and Electronics Engineers (IEEE): OAJ / IEEE

Publication year: 2021

Language: English

Related journal or series: IEEE Access

eISSN: 2169-3536

JUFO level of this publication: 2

Digital Object Identifier (DOI): http://dx.doi.org/10.1109/ACCESS.2021.3111118

Open Access: Open Access publication


Abstract

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.


Last updated on 2021-21-10 at 12:16