Citation: Abreha, H.G.; Hayajneh,
M.; Serhani, M.A. Federated Learning
in Edge Computing: A Systematic
Survey. Sensors 2022, 22, 450.
https://doi.org/10.3390/s22020450
Academic Editors: Matteo Anedda
and Daniele Giusto
Received: 22 November 2021
Accepted: 31 December 2021
Published: 7 January 2022
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Review
Federated Learning in Edge Computing: A Systematic Survey
Haftay Gebreslasie Abreha , Mohammad Hayajneh and Mohamed Adel Serhani *
Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates
University, Al Ain P.O. Box 15551, United Arab Emirates; 202090183@uaeu.ac.ae (H.G.A);
mhayajneh@uaeu.ac.ae (M.H.)
* Correspondence: serhanim@uaeu.ac.ae
Abstract:
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is
widely used in several applications. However, in conventional DL architectures with EC enabled,
data producers must frequently send and share data with third parties, edge or cloud servers, to
train their models. This architecture is often impractical due to the high bandwidth requirements,
legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged
as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and
legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles,
hospitals, and more, through a centralized server, while maintaining data localization. FL can
therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning
and model optimization. Although the existing surveys have taken into account applications of FL
in EC environments, there has not been any systematic survey discussing FL implementation and
challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on
the implementation of FL in EC environments with a taxonomy to identify advanced solutions and
other open problems. In this survey, we review the fundamentals of EC and FL, then we review the
existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework,
and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the
applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant
case studies of applying FL in EC, and we identify open issues and potential directions for future
research. We believe this survey will help researchers better understand the connection between FL
and EC enabling technologies and concepts.
Keywords:
federated learning; edge computing; intelligent edge; edge AI; data privacy; data security
1. Introduction
According to Cisco, the number of connected IoT devices could exceed 75 billion by
2025, which is 2.5-times the amount of data produced in 2020 (i.e., 31 billion) [
1
]. Further-
more, IoT devices are equipped with heterogeneous and advanced sensors for various
crowdsensing applications such as smart industry [
2
], healthcare [
3
], and Unmanned Areal
Vehicle (UAV) [
4
] applications. In addition, the demand for time- and quality-sensitive
IoT applications is overwhelming currently, which requires an infrastructure with high
availability and resilience. However, managing massive, heterogeneous, and distributed
IoT data and providing services at a specified performance with cloud infrastructure looks
impossible. Edge Computing (EC) is a new architecture that extends Cloud Computing
(CC) services closer to data sources, which reduces the latency and bandwidth cost and
improves the resilience and availability of the network [
5
–
7
]. Thus, time-critical applica-
tions with specified Service Level Agreement (SLA) demands can be fulfilled by leveraging
the EC-enabled architecture. In addition, EC is a distributed computing paradigm that
can handle the proliferation of IoT data and take advantage of distributed heterogeneous
computing resources. EC combined with Deep Learning (DL) [
8
] is a promising technology
and is widely used in several applications.
Sensors 2022, 22, 450. https://doi.org/10.3390/s22020450 https://www.mdpi.com/journal/sensors
