## 4 Discussion--Open Issues

Table 2: Categorization of indicative presented studies. in the literature. In the first case, the use of open-access protocols is favored. In the second case, aggregated data from heterogenous resources are sent to the data management layer for proper preprocessing prior to manipulation from the business or functional layer. In both cases, data collection from diverse devices and entities may lead to additional security threats, since not all parts have the capability to execute advanced security protocols. In this case, the use of blockchain technology is preferred, with efficient task offloading among the IoT devices and edge/cloud servers. In this context, IoT data are not directly inferred to the 6G network but are encrypted with blockchain from edge/cloud servers. Data collection can be also leveraged with the appropriate extension of well-known 5G NFs, such as the NWDAF [82], which has already been defined in the 5G architecture from release 15 of 3 GPP. NWDAF can perform data collection from various NFs. When combined with advanced AI/ML techniques, full-scale network optimization can be supported based on traffic demands and service requirements [83]. NWDAF can be deployed with other key enabling technologies, such as O-RAN, and support large-scale data collection and optimization in a secure and trusted environment. Based on the previous discussion, a conceptual 6G framework is depicted in Figure 9 (the application layer has been omitted for illustration purposes). To this end, various CF deployments are considered in the 6G area of coverage. Each CPU is connected to a separate NWDAF instance that collects all data related to the specific deployment. The FL-agent provides full network optimization in all CF segments. Aggregated data are also used in the service layer, where the DT agent executes multiple simulation scenarios to deal with various network instances and configurations, as well as in the microservice aggregator, which optimizes resource usage. Finally, the security agent can perform various tasks related to threat prediction and mitigation based on the outcomes of the FL agent, such as malicious node exclusion and intent-based creation for network recovery. However, also from the analysis of the previous sections, certain open issues and limitations can be identified. These are summarized below:  
1. Deployment capabilities and costs throughout large geographical areas. To this end, the support of ultra-high data rates with minimal latency necessitates dense deployments that may increase the cost of 6G infrastructures. Figure 9: A conceptual 6G framework. In the same context, the 6G approach should be also adopted by lightweight devices that will have the capability to run light versions of the 6G architecture. * Although ML models are a key innovation approach in 5G/6G networks, improved model performance is often accompanied by increased model complexity. In this context, a key innovation over the last few years is the concept of explainable artificial intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret ML models [84; 85]. The main focus is on the reasoning behind the decisions or predictions made by the AI algorithms to make them more understandable and transparent. Therefore, XAI assists in making ML models lead to decisions that are not based on irrelevant or otherwise unfair criteria. * Integration of various cutting-edge technologies. As discussed thoroughly in this article, a key concept in 6G networks will be the integration of various technologies, both in the physical and in the network layer. Hence, a challenging issue would be to limit overall complexity and signaling burden. For example, since 6G networks involve the collection of a vast amount of data, appropriate processing algorithms are required that can effectively manage this volume. In the same context, more complex transceiver designs are required to support transmission in much larger frequencies compared to 5G, as is the concept of ultra-dense massive MIMO systems.