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

本文介绍了联邦学习技术与移动边缘计算的应用，讨论了在大规模和复杂的移动边缘网络中实现联邦学习所面临的挑战以及现有解决方案，探讨了联邦学习在移动边缘网络优化中的应用，以及未来的研究方向与挑战，包括保护隐私与安全。