Federated Learning in Mobile Edge Networks: A Comprehensive Survey
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Abstract (arXiv)
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. Traditional cloudbased 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
Key facts. Cited by 408 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Sep 2019.
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Citing papers per year: 2021: 89; 2022: 77; 2023: 96; 2024: 67; 2025: 43; 2026: 36.
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- 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks31 Jul 2026
- 2026Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub21 Jul 2026
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