To facilitate the deployment of machine learning in resource and privacy-constrained systems such as the Internet of Things, federated learning (FL) has been proposed as a means for enabling edge devices to train a shared learning model while promoting privacy. However, Google's seminal FL algorithm requires all devices to be directly connected with a central controller, which limits its applications. In contrast, this article introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller. The fundamentals of this framework are developed and a number of communication techniques are proposed so as to improve CFL performance. An overview of centralized learning, Google's FL, and CFL is presented. For each type of learning, the basic architecture as well as its advantages, drawbacks, and operating conditions are introduced. Then four CFL performance metrics are presented, and a suite of communication techniques ranging from network formation, device scheduling, mobility management, to coding are introduced to optimize the performance of CFL. For each technique, future research opportunities are discussed. In a nutshell, this article showcases how CFL can be effectively implemented at the edge of large-scale wireless systems.

中文翻译：

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协作联邦学习的无线通信

为了促进机器学习在资源和隐私受限系统（如物联网）中的部署，联邦学习 (FL) 已被提议作为一种手段，使边缘设备能够在促进隐私的同时训练共享学习模型。然而，谷歌开创性的FL算法要求所有设备都直接与中央控制器相连，这限制了其应用。相比之下，本文介绍了一种新的 FL 框架，称为协作 FL (CFL)，它使边缘设备能够在较少依赖中央控制器的情况下实现 FL。开发了该框架的基础，并提出了许多通信技术，以提高 CFL 性能。介绍了集中式学习、Google 的 FL 和 CFL 的概述。对于每种类型的学习，介绍了基本架构及其优点、缺点和操作条件。然后介绍了四个 CFL 性能指标，并引入了一套从网络组建、设备调度、移动性管理到编码的通信技术，以优化 CFL 的性能。对于每种技术，讨论了未来的研究机会。简而言之，本文展示了如何在大规模无线系统的边缘有效地实施 CFL。讨论了未来的研究机会。简而言之，本文展示了如何在大规模无线系统的边缘有效地实施 CFL。讨论了未来的研究机会。简而言之，本文展示了如何在大规模无线系统的边缘有效地实施 CFL。