Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144291
Title: Federated learning in mobile edge networks : a comprehensive survey
Authors: Lim, Bryan Wei Yang
Luong, Nguyen Cong
Hoang, Dinh Thai
Jiao, Yutao
Liang, Ying-Chang
Yang, Qiang
Niyato, Dusit
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Lim, B. W. Y., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., ... Miao, C. (2020). Federated learning in mobile edge networks : a comprehensive survey. IEEE Communications Surveys and Tutorials, 22(3), 2031-2063. doi:10.1109/COMST.2020.2986024
Journal: 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.
URI: https://hdl.handle.net/10356/144291
ISSN: 1553-877X
DOI: 10.1109/COMST.2020.2986024
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/COMST.2020.2986024
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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