# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 4 Knowledge-Driven Applications in Telecommunications
### Network Optimization  
Apart from dealing with network demands in terms of operations and automation, telecommunication systems, especially the RAN domain, are expected to support an increased number of differentiated businesses and a massive amount of devices and services. In such a scenario, the complexity of resource allocation decisions will increase beyond existing RAN computational and storage capacity, hence making it difficult to efficiently optimize network configuration and planning. To address the increasing complexity, AI-driven network optimization based on machine learning (ML) techniques has been proposed. Trained on large datasets, generalized ML models are generated with relatively high accuracy and precision. Although the training can be performed centrally, in many cases within RAN, a centralized approach is prohibitive due to requirements on cost, latency, and bandwidth. In the more general case, privacy preservation concerns and/or regulatory limitations advocate in favor of a distributed learning solution such as federated learning, split learning, or distributed learning based on DNN, DQN, DRL, etc. In the case of RAN, distributed learning is also mandated by the nature of RAN, which is inherently distributed. Furthermore, with the recent advances of distributed learning [42], it is now possible to address these challenges and exploit the knowledge that is distributed in many network nodes. As depicted in Figure 5, decentralized learning can be divided into three main categories:* _Federated learning_--In federated learning (FL), multiple (possibly large numbers of) clients participate in the training of a shared global model by exchanging model updates with a central parameter server (PS) [43]. This is performed by using an iterative process where each global iteration, often referred to as _communication round_, is divided into three phases: 1. The PS sends the current model parameter vector \(\mathbf{x}\) (an \(M\)-dimensional vector) to all participating clients. Either the model is sent directly, or a differential compared to the previous round is transmitted. 2. The clients, the total number of which is \(N\), perform one or more steps of stochastic gradient descent on their own (private) training data and obtain a model update. Specifically, the \(n\)th client computes a gradient update \(\mathbf{x}_{n}\) (\(M\)-dimensional vector). 3. The \(M\)-dimensional model updates \(\mathbf{x}_{1},\dots,\mathbf{x}_{N}\) from the \(N\) clients are then sent back to the PS. The PS aggregates these updates by adding them together to obtain the updated model \(\mathbf{x}\). A typical aggregation rule is to compute a weighted sum of the updates, that is, compute the M-dimensional total update \(\sum_{n=1}^{N}\alpha_{k}\mathbf{x}_{n}\) for some weights \(\alpha_{k}\). Other non-linear aggregation rules are also possible, for example, to safeguard against malicious or malfunctioning agents [44]. The above procedure is repeated until a termination criterion is met. The termination can, for example, be when a desirable training accuracy is achieved, when a predefined number of iteration rounds have been reached, or when a global loss function meets the minimum requirements. The loss function depends on the computational problem intended to be solved using the FL system, and typically, it represents a prediction error on a test dataset. The above procedures are depicted in Figure 5.  
* _Split learning_--Split learning (SL) is another cooperative ML technique used to train a neural network model between a client and a central server by splitting the model at a cut layer into a client-side model and a server-side model [45]. The existing SL approach conducts the training process sequentially across clients, where each client calculates the intermediate results and transmits them to the next client in order. Each client passes the intermediate gradient in backward order when casting backward propagation.