# Overview of Distributed Machine Learning Techniques for 6G Networks
## 1 Introduction  
The rapid growth of data and information availability in recent years produced a paradigm change in wireless networks: \"big data\" cannot be anymore managed as a whole. New parceled \"small data\" should be envisaged where large amounts of data are distributed among several nodes for their processing [1]. The 6G standard will provide worldwide connection, reduced latency, and allow the growth of applications based on extremely dense and diverse wireless networks, such as Internet of Things (IoT) systems [1, 2]. Intelligent interactions between devices across the network, which is the goal of developing distributed machine learning (ML) methods, will be essential to manage and exploit this continuously increasing amount of data and communications (often from machine to machine) within networks that are not in optimal conditions. In these kinds of applications, there is no need to send large amounts of data to a central system that produces the learning model; instead, data processing and local observations are handled by the devices themselves at the network's edge. There are various advantages to such solutions: a significant reduction in transmission load and the possibility to optimize how resources are utilized, in addition to cost saving [3]. In contrast to older generations, the 5G wireless network brings in a truly digital society by achieving substantial advancements in latency, data rates, mobility, and the number of connected devices. The 5G network with integrated technologies, such as IoT,mobile edge computing (MEC), fog computing, and ML, spans different domains of society, including robotics [4], vehicular networks [5], healthcare [6], etc. 5G is predicted to reach its limit in a decade; hence, the academia and the standardization bodies are already working toward a new generation of wireless communications, named 6G [7]. The key technical objectives for 6G networks will be:  
* Ultra-high data rate (up to 1 terabit per second (Tbps)) and ultra-low latency communications. * High energy efficiency for resource-constrained devices. * Ubiquitous global network coverage. * Trusted and intelligent connectivity across the whole network. The _space_ of needs for 6G applications will encompass a wide range of throughput, latency, dependability, scalability, service availability, service continuity, and security dimensions [8]. Simultaneously, obtaining a significantly higher resource efficiency than in 5G is a critical step, not only toward expanded spectrum capacity, but also toward meeting the ambitious energy reduction goals for future networks. As a result, the design of 6G radio access must be flexible and resource efficient, having the ability to be adjusted in real-time. As an example, multi-antenna communication is a crucial component of tomorrow's wireless networks. Cellular multiple-input multiple-output (MIMO) and decentralized extensions conform to the old cellular paradigm, which is inefficient in dense networks due to overly frequent handovers and pilot sequence re-assignments. This encourages a new user-centric cell-free MIMO strategy, in which the mobile device fluidly navigates through a _sea of access points_ that are dynamically associated. Another aspect to be considered in 6G is to achieve communications that use terahertz-scale frequencies (THz). THz communication allows one to cover the spectrum well beyond 100 GHz and has been the focus of significant research for the past two decades [8]. While propagation studies and channel characterization were important at the start of this research, in recent years, various new approaches have been developed, and the feasibility of THz communications has been demonstrated. IEEE in 2017 published the world's first wireless standard (IEEE Std. 802.15.3d-2017 [9]), which operates at a frequency of roughly 300 GHz and has a bandwidth of up to 69 GHz [10]. This big block of the spectrum has the potential to support 1 Tbps data rates in the future without requiring overly sophisticated baseband processing.