论文标题
联合学习和无线通信
Federated Learning and Wireless Communications
论文作者
论文摘要
由于其强大的功能和潜在的应用，联合学习在无线通信和机器学习领域变得越来越有吸引力。与其他不需要通信资源的机器学习工具相反，联邦学习利用了中央服务器与分布式本地客户端之间的通信来培训和优化机器学习模型。因此，如何有效地分配有限的沟通资源来培训联合学习模型对于绩效优化至关重要。另一方面，作为一种全新的工具，联邦学习可能会增强无线网络的智能。在本文中，我们提供了有关联合学习和无线通信之间关系的全面概述，包括联合学习的基本原则，用于培训联合学习模型的有效沟通以及用于智能无线应用程序的联合学习。我们还确定了本文末尾的一些未来研究挑战和指示。
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model. Therefore, how to efficiently assign limited communication resources to train a federated learning model becomes critical to performance optimization. On the other hand, federated learning, as a brand new tool, can potentially enhance the intelligence of wireless networks. In this article, we provide a comprehensive overview on the relationship between federated learning and wireless communications, including basic principle of federated learning, efficient communications for training a federated learning model, and federated learning for intelligent wireless applications. We also identify some future research challenges and directions at the end of this article.
