[论文解读] Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Wei Yang Bryan Lim, Nguyen Cong Luong+6 more|arXiv (Cornell University)|Sep 26, 2019

Privacy-Preserving Technologies in Data参考文献 202被引用 144

一句话总结

This survey reviews Federated Learning (FL) for mobile edge networks, detailing FL fundamentals, challenges, solutions, and applications for edge optimization. It also discusses privacy, security, and future research directions.

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. 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

研究动机与目标

• 以在边缘进行协作式机器学习训练为隐私保护范式来激励 FL。
• 总结与移动边缘网络相关的 FL 基本原理、协议和框架。
• 回顾 FL 在通信效率、异构性以及隐私/安全方面的挑战。
• 探讨 FL 在移动边缘网络优化与管理中的应用。
• 概述在 MEC 领域中 FL 的开放挑战和未来研究方向。

提出的方法

• 解释带有本地训练和全局聚合的 FL 训练过程，其中 FedAvg 作为核心算法。
• 讨论非 IID 数据的统计挑战及提出的解决方法（例如数据共享、数据增AR、再平衡）与多任务学习方法。
• 介绍 FL 协议和框架考虑因素，包括参与者选择、配置和汇报阶段。
• 回顾在 FL 内部对通信成本和资源分配策略的优化。
• 概述隐私与安全问题及潜在的缓解技术。
• 调研 FL 在移动边缘网络优化任务中的应用，例如资源管理和卸载。

实验结果

研究问题

• RQ1 在移动边缘网络中，联邦学习的关键基本要素和工作流程是什么？
• RQ2 在异构边缘设备上大规模部署时，FL 会出现哪些挑战，存在哪些解决方案？
• RQ3 如何在保护隐私和安全的前提下，利用 FL 来优化移动边缘网络？
• RQ4 在 MEC 环境中，FL 的开放研究方向和未来挑战有哪些？
• RQ5 哪些框架和实际考虑因素能实现边缘基础设施上可扩展的 FL 实现？

主要发现

• FL 使原始数据保留在设备上就能进行协作式模型训练，降低数据传输量和延迟。
• 参与者之间的非 IID 数据带来准确性和收敛性挑战，需要诸如数据共享、数据增强，或基于多任务学习的个性化方法。
• FedAvg 是基础聚合方法，具有解决异质性、收敛性和个性化的扩展（例如 FedProx、MOCHA、FEDPER）。
• 通信效率和设备异构性仍然是核心挑战，促使协议层面的解决方案和资源感知的训练策略。
• FL 作为移动边缘网络优化的使能技术具有强大潜力，包括蜂窝选择、计算卸载和车载网络管理等任务，同时也引发隐私与安全方面的考虑。