## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP
#### NWDAF in Release 17  
According to the 3 GPP specifications in Releases 15 and 16, multiple NWDAF instances, within the same public land mobile network (PLMN), act independently of one another. However, calculated analytics at nearby geographical areas and/or different analytics events may be related to each other, e.g., UE mobility and communication analytics are correlated with network data analytics. In addition, the sheer amount of generated data in future networks would rather impose a distributed architecture, where multiple NWDAF instances, placed at the edge and core of networks, cooperate with each other. Recall that the virtualised NFs are programmable and therefore, can be located almost anywhere in the network. Different NWDAFs may be assigned different tasks or share the computational load for the same analytic event. Furthermore, NWDAFs located at the edge can monitor the network, obtain useful analytics, and maintain the QoE, even if the connectivity to the central cloud is lost. Recall that communication to the cloud also increases latency; time-critical services, e.g., autonomous driving, are likely to require an NWDAF placed near the edge. For instance, an edge NWDAF might quickly perform real-time analytics, while a centralised NWDAF is suitable for carrying out data processing, selection, and training of ML models. Federated learning is a distributed learning algorithm, where several learners are locally trained using their own datasets and communicate only their model parameters to a server. The server aggregates the received models to form the global model that is sent back to the learners for retraining. This process iteratively continues until a convergence constraint is satisfied. Federated learning was invented by Google for image classification and language modelling tasks and showed remarkable performance in next word prediction [69]. One should not confuse federated learning with other distributed learning methods, e.g., split learning [70], where clients with low computational capabilities, such as IoT, train only the first few layers of a DNN and send the extracted features to a gateway or to the cloud, which completes the computationally demanding part of the training on behalf of the clients. Federated learning can become a bandwidth-efficient and privacy-preserving distributed learning technique for communication networks. The datasets collected by the learners can vary in size and do not need to be independent of each other, which is often the case in communication networks with the learners being either distributed NFs at the core or UEs at the RAN. In [71], federated learning is used for predicting popular video content for edge caching and is performed closely to the centralised baseline scheme. The 5G core network has an inherent built-in support for distributed learning. Specifically, the various NFs can be viewed as learners that collect data and locally train ML models about specific analytic events, e.g., UE mobility, communication, and session management, while the NWDAF can be viewed as the server that supports global analytics, e.g., learning to optimise the allocation of network slices. Furthermore, in 3 GPP Release 17, multiple  
Figure 4: Example illustration for distributed implementation of NWDAF in 3 GPP Release 17, using federated learning [68]. Three distributed NWDAF instances at the edge, occupying different geographical areas, perform local learning and share their NN weights \(w_{l},l\in\{1,2,3\}\) with the centralised NWDAF at the cloud, which averages the received models and sends back to the local learners the global model \(w_{g}\) for re-training. -NWDAF clients can form a federation and train ML models in a distributed and hierarchical manner [68]; see Figure 4.  
Federated learning significantly reduces the communication signalling overhead as compared to the case where raw data are exchanged between the server and the learners.