# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 4 Knowledge-Driven Applications in Telecommunications
### Network Optimization
Furthermore, relevant features from the data are then transmitted to the  
Figure 5: Distributed learning approaches for network optimization. (**a**) Federated learning setup, with a central parameter server and a number of clients that participate in the training of a machine learning model located in the server; (**b**) split learning setup, with a central parameter server. Here, all clients have a copy of the partial model and cooperate in training the model in a sequential order; (**c**) fully distributed learning setup consisting only of clients each having a local copy of the model and cooperating in training the model via direct communication. central server which processes these features, performs the necessary computations, and sends back the updated parameters. This approach allows the sensitive data to remain on the client's side, ensuring privacy, while still benefiting from the centralized server's computational power. Split learning maintains a partition between data and computations, safeguarding user privacy during model training [46]. A depiction of SL is illustrated in Figure 5b;
* _Fully distributed learning_--In fully distributed learning setups, clients collaboratively perform learning without transmitting model updates to a central parameter server [47]. Instead, all clients have their own instance of the learning model. Each client has its own private training data. The clients are connected according to a connectivity graph such that each client has one or more neighbors. This scenario is illustrated in Figure 5c. The training proceeds by exchanging updates between the clients and their neighbors using, for instance, gossip-based averaging, where nodes iteratively share and merge information to collectively refine their local models. Fully distributed learning operates in a decentralized manner, promoting data locality and fostering collaboration among neighboring nodes while avoiding a central server's reliance [48]. Wireless networks often have limited bandwidth and high latency; therefore, distributed learning is beneficial. Firstly, it minimizes data transmission by processing data locally on sites, which reduces the risk of exposing sensitive information. Instead of transmitting raw data to a central server, only model updates or aggregated information are shared. Secondly, utilizing multiple sites allows for scaling machine learning tasks while reducing latency in decision-making and increasing system efficiency and robustness. If a site in the network fails or has no available computational resources, the overall learning process can continue without significant interruptions. Finally, transferring learned models creates a smaller network footprint than transferring raw data, thus offloading the user plane. A combination of a low network footprint and better resource utilization yields energy savings. Future research in distributed learning needs to address data, system, and model heterogeneity. Data heterogeneity relates to challenges with domain adaptation and identical and independent distributions, (i.i.d). System heterogeneity refers to system-related inconsistencies encountered between different sites that may skew the overall process in terms of robustness and resilience. Whilst, model heterogeneity refers to FL with different model architectures.