## 2 6G Key Enabling Technologies
### Al/ml  
AI/ML approaches are already in use in a wide range of potential applications in 5G networks [33]. To this end, data collected directly from various network entities are used to train appropriate ML models in three ways: (a) supervised learning, where model training takes place with the help of a labeled data set, (b) unsupervised learning, where the goal is to extract data patterns, and (c) reinforcement learning (RL). The latter case is based on the existence of a mobile agent that interacts with the environment under consideration and assigns certain rewards or penalties for specific actions. In most use cases, however, the set of potential actions-results can be quite large, and look-up tables can be hardware-consuming. In this case, training with the help of neural networks (NNs) is favored, a concept that is also known as deep reinforcement learning (DRL). In the context of 6G networks, AI/ML can contribute to various operations, such as security and privacy protection via the extraction of abnormal patterns [27], as well as in network reconfiguration and optimization when needed [34]. However, the development of 6G networks brings forward several research gaps that need to be addressed to fully leverage AI-enabled technologies [35]. Since 6G networks are based on the integration of various heterogeneous networks and devices, radio resource management (RRM) can be a highly complex task due to the multiple associated parameters. Hence, a large amount of real network data is required for fast decision-making. In the same context, privacy concerns when data exchange takes place in ML model training is another issue of debate. Finally, high resilience, scalability, and flexibility in learning frameworks are essential for sustaining an infinite number of interacting entities and providing high-quality services in actual dynamic 6G networks. As previously mentioned, since 6G networks are expected to integrate and process a large amount of data from heterogeneous resources, data collection and processing in a single node might significantly increase computational burden and training times. Moreover, such an approach would be prohibitive, since it results in a single point of failure. Therefore, over the last few years, the concept of federated learning (FL) [36] has emerged as an alternate approach that can leverage execution times and relax computational burden. To this end, several participating nodes locally train an ML model based on the data collected from their surrounding environment. Afterwards, ML model parameters, such as weights in the case of NN training, are sent to a master ML node for aggregation and updating. The master node, after processing and aggregating all parameters, sends the updated weights to the participating nodes. Therefore, on one hand, the computational burden is divided among the participating nodes, and on the other hand, no sensitive data are transmitted. Hence, privacy-sensitive applications such as e-health can now be deployed more easily. An overview of FL is depicted in Figure 2. To this end, a three-layered approach is considered that includes training nodes, multi-access edge computing (MEC) servers, and cloud servers. Therefore, model parameters are locally generated and then updated at the MEC level. Afterwards, master model aggregation in the cloud domain takes place as well. This approach can be highly beneficial when fast training times are required, since MEC servers are located close to the participating training nodes. Consequently, model updates based on MEC processing generally undergo a short round-trip time. However, there are certain challenges related to the proper deployment of FL in next-generation networks. First, although, as previously mentioned, transmission of sensitive information does not take place, it is still possible to reveal such information through the parameters that are exchanged during the learning process.