Cited by 2,565Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingDomain Adaptation and Few-Shot Learning

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Open access
Lim, Wei Yang Bryan, Luong, Nguyen Cong, Hoang, Dinh Thai, Jiao, Yutao, Liang, Ying-Chang, Yang, Qiang, Niyato, Dusit, Miao, Chunyan·2020·IEEE Communications Surveys & Tutorials

Abstract

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

Integrity signals

Registry

Crossref · 10.1109/comst.2020.2986024

Cites

194 of 194 references verified0 citation errors · 0 not checkable

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  • Federated learning is a distributed machine learning framework that can train a unified deep learning model across multiple decentralized nodes, where each node holds its own local data samples for training, and those samples never need to be exchanged with any other nodes.

    “Federated learning is a distributed machine learning framework that can train a unified deep learning model across multiple decentralized nodes, where each node holds its own local data samples for training, and those samples never need to be exchanged with any other nodes [94].”

    From Machine Unlearning: A Survey (2024) · cites this paper as [94]

  • FL technique assures user privacy and data security because it does not access raw data.

    “This technique assures user privacy and data security because it does not access raw data [21].”
  • Global convergence is still guaranteed in CMFL.

    “Global convergence is still guaranteed.”
  • Authors in [29] have come up with CMFL algorithm, which reduces the cost of communication by only uploading updates of the local model that are relevant.

    “Authors in [29] have come up with the Communication-Mitigated Federated Learning (CMFL) algorithm, which reduces the cost of communication by only uploading updates of the local model that are relevant.”
  • By doing so, FedProx limits the impact of non-independent and identically distributed (non-IID) device data on the global learning model.

    “By doing so, FedProx limits the impact of non-independent and identically distributed (non-IID) device data on the global learning model.”

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Authors
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C.
Affiliations
  • Nanyang Technological University (SG)
  • Phenikaa (Vietnam) (VN)
  • University of Technology Sydney (AU)
  • University of Electronic Science and Technology of China (CN)
  • Hong Kong University of Science and Technology (HK)
Venue
IEEE Communications Surveys & Tutorials
Published
2020
Volume
22
Issue
3
Pages
2031-2063
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
OpenAlex
W3015636663
Cited by
2,565
References
194
Citations by year
2026: 337 · 2025: 409 · 2024: 443 · 2023: 489 · 2022: 416 · 2021: 326 · 2020: 144 · 2019: 1
FWCI
135.14
Type
journal article
Language
en
Open access
Locations
  • IEEE Communications Surveys & Tutorials (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • UTS ePRESS (University of Technology Sydney) (submittedVersion, public-domain)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Research Foundation (NRF), Singapore, through Singapore Energy Market Authority, Energy Resilience (NRF2017EWT-EP003-041, NRF2015-NRF-ISF001-2277)
  • Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure (NSoE DeST-SCI2019-0007)
  • A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing (RGANS1906, WASP/NTUM4082187(4080))
  • Singapore MOE Tier 2 (MOE2014-T2-2-015 ARC4/15, MOE Tier 1 2017-T1-002-007 RG122/17)
  • AI Singapore Programme (AISG-GC-2019-003, NRF-NRFI05-2019-0002)
  • Alibaba-NTU Singapore Joint Research Institute (Alibaba-NTU-AIR2019B1)
  • Nanyang Technological University, Singapore
  • Vietnam National Foundation for Science and Technology Development (102.02-2019.305)
  • National Natural Science Foundation of China (61631005, U1801261)
  • National Basic Research Program of China (2018YFB1801105)
  • Higher Education Discipline Innovation Project (B20064)
  • Hong Kong CERG (16209715, 16244616)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
federated learning, mobile edge computing, mobile edge networks, data privacy, resource allocation, communication overhead, heterogeneous devices, deep learning, vehicular networks, medical applications

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