Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and the massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, accuracy, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.

中文翻译：

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无线通信网络的分布式机器学习：技术、架构和应用

分布式机器学习 (DML) 技术，例如联邦学习、分区学习和分布式强化学习，已越来越多地应用于无线通信。这是由于终端设备能力的提高、数据量的爆炸式增长、无线接口的拥塞以及对数据隐私的日益关注。无线系统的独特特性，如大规模、地域分散部署、用户移动性和海量数据，给DML技术的设计带来了新的挑战。现有文献中存在明显的空白，即 DML 技术尚未对其在无线系统中的适用性进行系统审查。本次调查通过提供对 DML 技术的现代和全面调查来弥补这一差距，重点是无线网络。具体来说，我们回顾了 DML 在功率控制、频谱管理、用户关联和边缘云计算方面的最新应用。分析了DML的最优性、准确性、收敛速度、计算成本和通信开销。我们还讨论了 DML 应用程序面临的潜在对抗性攻击，并描述了保护隐私和安全的最先进对策。最后但并非最不重要的一点是，我们指出了许多尚未解决的关键问题，并为未来的研究整理了潜在的有趣和具有挑战性的主题。我们还讨论了 DML 应用程序面临的潜在对抗性攻击，并描述了保护隐私和安全的最先进对策。最后但并非最不重要的一点是，我们指出了许多尚未解决的关键问题，并为未来的研究整理了潜在的有趣和具有挑战性的主题。我们还讨论了 DML 应用程序面临的潜在对抗性攻击，并描述了保护隐私和安全的最先进对策。最后但并非最不重要的一点是，我们指出了许多尚未解决的关键问题，并为未来的研究整理了潜在的有趣和具有挑战性的主题。