## 1 Introduction  
Recently, Machine Learning (ML) techniques, especially those belonging to the Distributed Learning (DL) class, have gained huge popularity in dynamic wireless scenarios such as the Internet of Vehicles (IoV) with their added advantages in terms of learning efficiency, reliability, and data security [1]. With this, various DL methods, such as Federated Learning (FL), Multi-Agent Learning, and Collaborative Learning, are considered in vehicular domains [2]. Additionally, various ML tools and techniques have been considered to form suitable DL methods, such as multi-agent FL, DL with model split, DL with meta-Learning, and DL with swarm learning. In this way, a rich ecosystem of DL methods with specific characteristics, performance, and demand is formed and made available to serve users [3]. From a networking point of view, several new advances have recently been introduced, especially with the innovations of 5G and B5G technologies. Different computing paradigms, such as Edge/Cloud Computing, have been introduced to implement new services and applications with better performance [4]. Technologies, such as network soft-warization through Network Function Virtualization (NFV), Software Defined Networking(SDN), and Network Slicing (NS), have revolutionized the networking process and opened the doors to a multitude of applications and services with different demands and additional flexibility [5]. Furthermore, distributed computing and communication technologies, such as the edge-to-cloud continuum [6], and joint Terrestrial and Non-Terrestrial Networks (T/NTN) [7], have gained huge popularity in terms of capacity, coverage, and reliability for serving end users. In the realm of advanced 6G technologies for NTN and satellite-terrestrial integrated networks, the contributions are notable as follows. Ref. [8] delves into secrecy-energy-efficient hybrid beamforming, proposing robust schemes for single and multiple earth stations. The authors in [9] focused on a destructive beamforming design, introducing low-complexity schemes for known and unknown malicious reconfigurable intelligent surfaces (RIS). Ref. [10] addresses joint beamforming for hybrid satellite-terrestrial relay networks, optimizing power while ensuring user rate requirements. Lastly, the authors in [11] proposed a multilayer RIS-assisted secure integrated terrestrial-aerial network architecture, maximizing system energy efficiency and defending against attacks. Shifting focus to broader aspects of wireless communication, some of the latest key technologies that contribute to the applications of different 6G techniques in Intelligent Transportation Systems (ITS) and IoV, and the vision for 6G-enabled smart cities are as follows. Ref. [12] emphasizes cooperation between cognitive users for better cognitive radio network performance. The authors of [13] explored the role of 3 GPP in the evolution of cellular communication, highlighting the sharing of resources in the context of the Internet of Vehicles. Ref. [14] delves into interference in cognitive radio networks, discussing terminologies and mitigation techniques. Finally, Ref. [15] provides a visionary perspective on 6G-enabled Internet of Things (IoT) networks for sustainable smart cities, incorporating artificial intelligence, machine learning, and novel architectures. These technologies, along with different ML methods, are creating a new paradigm, known as Edge Intelligence, to enable distributed near-user intelligent wireless networks in different domains [16]. However, there are still some gaps between the potential of these innovative technologies and their possible uses to create a safe, reliable, and intelligent vehicular system. When focusing on the IoV scenario, users demand an increased number of services, each tailored to specific requirements [17]. For example, autonomous driving may require huge data processing and low latency, while infotainment applications may require ultra-broadband connections.