6G的联邦学习：应用、挑战和机遇
Federated Learning for 6G: Applications, Challenges, and Opportunities
摘要：
传统的机器学习集中在云端（数据中心）。近年来，无线网络中的安全问题以及丰富的数据和计算资源的可用性正在推动学习算法向网络边缘的部署。这导致了一个快速增长的领域的出现，称为联邦学习（FL），它集成了两个原本分离的领域：无线通信和机器学习。在本文中，我们提供了一个全面的研究FL的第六代（6G）无线网络的应用。首先，我们讨论了应用FL无线通信的关键要求。然后，我们专注于激励应用FL的无线通信。我们确定的主要问题，挑战，并提供了一个全面的治疗实施FL技术的无线通信。
英文摘要：
Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms towards the network edge. This has led to the emergence of a fast growing area, called federated learning (FL), which integrates two originally decoupled areas: wireless communication and machine learning. In this paper, we provide a comprehensive study on the applications of FL for sixth generation (6G) wireless networks. First, we discuss the key requirements in applying FL for wireless communications. Then, we focus on the motivating application of FL for wireless communications. We identify the main problems, challenges, and provide a comprehensive treatment of implementing FL techniques for wireless communications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
