## 1 Introduction  
The upcoming 6G technology is expected to create an intelligent, fully connected, and digitized society through large-scale deployments of distributed intelligent networks [1]. This is expected to create a plethora of new intelligent services and applications with specific demands. Internet of Things (IoT) technology has foreseen great success through enabling 5G solutions and is expected to play a key role in 6G society [2]. With the integration of IoT subcases into different wireless scenarios, many sensory nodes are expected to be deployed in the 6G world, with the ability to sense nearby environments and produce tons of high-quality data. This data can be extremely important to enable intelligent solutions in the 6G networks [3]. Machine Learning (ML) is another important technology that has acquired a central role in the 6G vision; in particular, to enable intelligent services [4, 5]. Various ML methods are expected to be deployed in different wireless scenarios to enable intelligent solutions. ML can be extremely useful for analyzing the 6G network data and harnessing its intelligence. The Centralized Learning (CL) method is one of the most common approaches to generating AI algorithms in distributed communication scenarios. This involves concentrating all training data on a single server, which can complete the model's training individually due to its high computing power. However, this solution may not be particularly efficient when analyzed from a 6G perspective. Next-generation networks will need to handle large amounts of information, most of which will be produced by devices at the network's edge. In this scenario, moving data to a central server is expensive in terms of communication resources and represents a potential risk of network overload. It can induce large communication overheads and has limited applicability in the resource-constrained and time-critical 6G world. Furthermore, privacy and security restrictions can also limit the possibility of data transmissions to server nodes. A more efficient alternative is offered by Distributed Learning (DL), which is based on decentralization of training [5]. In this approach, the training phase will be carried out directly on end devices or possibly on specific nodes located strategically on the edge of the network. DL allows for the training of models by processing the local data of each device directly within it. This approach enables the use of large amounts of heterogeneous data scattered throughout the vast network, involving a larger audience of users than the centralized variant. This approach also introduces additional advantages. First, it makes it possible to significantly reduce data flow, communication overhead, and network traffic. Second, there is greater protection for end-user privacy as data are processed directly on personal terminals. Finally, by distributing the computational load among the multitude of devices, it is also possible to obtain essential improvements in terms of energy management. Several forms of distributed learning, such as Federated Learning (FL), collaborative learning, split learning, and multi-agent reinforcement learning, are widely considered in wireless networks for enabling intelligence-at-the-edge. Among others, FL has achieved great success in building high-quality ML models based on dispersed wireless data [6; 7]. FL is also a candidate for next-generation 6G communication standard allowing for setting up an intelligence-at-the-edge framework [8]. The traditional FL approach includes a set of devices with datasets and a server node [6]. The server node initiates the learning process by defining the learning task, and corresponding model, which is then transmitted to FL devices. With the help of received model parameters from the server node (also called the global model), their datasets, and onboard computation capabilities, each FL device is expected to train the ML model locally. Then, the updates to the ML model from each device are sent to the server.