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.

中文翻译：

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移动边缘网络中的联邦学习：综合调查


近年来，移动设备配备了越来越先进的传感和计算能力。再加上深度学习 (DL) 的进步，这为有意义的应用开辟了无数的可能性，例如医疗目的和车辆网络。传统的基于云的机器学习 (ML) 方法需要将数据集中在云服务器或数据中心。然而，这会导致与不可接受的延迟和通信效率低下相关的关键问题。为此，人们提出了移动边缘计算（MEC），以使智能更接近数据产生的边缘。然而，移动边缘网络中机器学习的传统支持技术仍然需要与外部各方（例如边缘服务器）共享个人数据。最近，鉴于日益严格的数据隐私立法和日益增长的隐私问题，引入了联邦学习（FL）的概念。在 FL 中，终端设备使用本地数据来训练服务器所需的 ML 模型。然后，终端设备将模型更新而不是原始数据发送到服务器进行聚合。 FL 可以作为移动边缘网络中的一项使能技术，因为它可以实现 ML 模型的协作训练，并且还可以实现用于移动边缘网络优化的 DL。然而，在大规模且复杂的移动边缘网络中，涉及具有不同约束的异构设备。这给大规模实施 FL 带来了通信成本、资源分配以及隐私和安全方面的挑战。在本次调查中，我们首先介绍 FL 的背景和基础知识。然后，我们强调了 FL 实施的上述挑战并回顾了现有的解决方案。 此外，我们还介绍了 FL 在移动边缘网络优化中的应用。最后，我们讨论了 FL 的重要挑战和未来的研究方向。