articleIEEE Communications Surveys & TutorialsJan 1, 2020Closed access

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

WYWei Yang Bryan LimNCNguyen Cong LuongDTDinh Thai HoangYJYutao JiaoYLYing‐Chang Liang

Nanyang Technological University · Phenikaa (Vietnam) · +4 more institutions

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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…

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  • Computer science
  • Enhanced Data Rates for GSM Evolution
  • Telecommunications
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