Paper Info
Title
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond
Abstract
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.
Year
DOI
Venue
2021
10.1109/JIOT.2020.3030072
IEEE Internet of Things Journal
Keywords
DocType
Volume
Artificial intelligence (AI),deep learning (DL),distributed intelligence,federated learning (FL) applications,FL,machine learning (ML),privacy,resource management,security
Journal
8
Issue
ISSN
Citations 
7
2327-4662
15
PageRank 
References 
Authors
0.71
0
6
Name
Order
Citations
PageRank
SA Rahman1150.71
Hanine Tout2686.67
H Ould-Slimane3150.71
Azzam Mourad439538.80
chamseddine talhi519223.98
Mohsen Guizani66456557.44