# Overview of Distributed Machine Learning Techniques for 6G Networks
## 1 Introduction
Nonetheless, there are significant obstacles to overcome. To offset the high-path loss in this frequency band, high-gain antennas are required. This complicates THz communications for mobile applications, as device detection, beam steering, and beam tracking are more difficult in THz systems than in millimeter wave systems. Supporting the requirements of the large range of 6G applications and use cases is a major challenge. In addition, in order to support such new technological advancements, much higher intelligence levels and processing efforts should be encompassed. This is the reason why artificial intelligence (AI) and ML are considered as fundamental elements of the forthcoming 6G, and not just an optimization tool for the performance evaluation. However, the majority of today's AI/ML solutions use centralized learning, in which data are collected throughout the system but training is performed in a single spot. Centralized learning is not ideal and often too expensive on a distributed platform, such as in carrier networks. At the centralized data center, there are significant energy supply requirements to be covered by the responsible tenant/owner, and privacy concerns. As a result, as we move closer to 6G, it will be critical to design new procedures that will allow the system to function and perform well, and coordinate learning and execution across a pool of--possibly--computation, networking, storage, and energy resources. Engineering-wise, the ability to manage exceedingly varied resources, system dynamism, and efficient real-time learning are the primary obstacles to achieving this kind of technological development. In the algorithmic field, on the other hand, future objective solutions will include the ability to perform training using highly fragmented data, increase efficiency in dynamic environments, and manage collaborative learning transfer agents in order to optimize anomaly handling. The goal of this paper is to survey on the most influential papers on distributed learning approaches for 6G and wireless communications. We have surveyed the articles related to machine learning, wireless communication, applications of machine learning in wireless communication, upcoming 6G technology vision, distributed learning for 6G, privacy and security related issues in distributed learning, distributed learning over joint terrestrial and non-terrestrial networks, etc. Most of the articles are from well-known IEEE journals and magazine papers. We have considered the most recent papers that are from the last few years to avoid outdated information. In Section 2, we have described the various generations of wireless communication technology and presented the upcoming 6G network challenges. After recalling the fundamentals of ML in Section 3, we describe the main distributed learning approaches introduced so far (Section 4). In Section 5, we survey the most influential papers considering the applications of DL algorithms for 6G. In Section 6 we drive future directions for the usage of DL in 6G systems. In Section 7, we discuss the main points of the paper. Finally, in Section 8, we conclude the paper.