# Overview of Distributed Machine Learning Techniques for 6G Networks
## 3 Machine Learning in Wireless Communication
### Deep Learning with Artificial Neural Networks (DL-ANN)  
Deep learning is a sub-branch of the vast ML field containing several tools for extracting useful information from various types of datasets. With the recent big data revolution and innovations in hardware technologies, the deep learning field reemerged with the availability of two main ingredients: a large amount of data and greater processing power. Deep learning techniques can be used to achieve various ML tasks, including predictions [14], classifications [15], sequential decision making in uncertain environments [16], etc. The traditional ML methods heavily depend on the feature definitions. The deep learning algorithms can extract the important features from the raw data through the heretical processing of the sequence of layers constituted by the nonlinear processing units called neurons [17]. This extracted information can be used to achieve some predefined objective, i.e., prediction, classification, etc. Deep learning, in association with neural network implementations, has its origins in the previous century [18]. Based on the number of hidden layers present between input and output layers, neural network models can be classified into two groups. One group consists of a shallow neural network with limited hidden layers (mostly one or two). On the other hand, deep neural network models can have more number of hidden layers, which can also determine their complexity and performance. An artificial neural network, used for tasks such as logistic regression, can have an input layer where the inputs are stored, and output can be a single point towards which the inputs are directed. A neuron in an artificial neural network is a connected processing element that takes one or more weighted inputs and creates real-valued activations by passing them through the activation function [19]. The logistic regression can be modeled as a one-neuron neural network [18]. In general, simple neural networks can have several neurons, forming layers. Each neuron in a layer is connected to all neurons in the following layer, and their outputs get multiplied by a weight associated with particular connection. In general, weight specifies how much information from the previous layer needs to be passed on to the next. The weight is determined by the neuron-target pair, not by the neuron itself. This is to say that the weight \(w_{i,j}\) between neuron \(i\) and \(j\) can be different from \(w_{j,i}\), indicating the weight between neuron \(j\) and \(i\)--obviously from different layers. These weights almost always have distinct values. The information flows through the neural network from the first input layer to the output layer, passing through the hidden layer(s). As an example, we refer to a simple three-layered artificial neural (ANN) network [18]. The input layer consists of three neurons, each of which can accept an input value. The three variables used to represent them are \(x_{1},x_{2}\), and \(x_{3}\). The first layer performs only one operation: accepting input values. A single output is available for each of the first layer neurons. The neural network may have fewer input values than the first layer's neurons, but not the other way around. As stated previously, each neuron in the input layer is connected to all neurons in the hidden layer; however, neurons in the same layer are not interconnected. Each connection between neuron \(j\) in layer \(k\) and neuron \(m\) in layer \(n\) has a weight that we will denote as \(w_{jm}\). The weight determines how much of the starting value is sent to each neuron. The weight's value is not fixed; it can both decrease and enhance the input's value. When a neuron in the second layer receives many inputs, it receives the sum of each of them multiplied by the associated weight as a quantity. In the second layer's third neuron, the inputs are \(x_{3},x_{2}\), and \(x_{3}\); and the weights are \(w_{13},w_{23}\), and \(w_{33}\).