# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Collaborative Machine Learning for Energy-Efficient Edge Networks in 6G  
The authors of [64]  suggested a decentralized and collaborative ML architecture for 6G edge networks, which allows agents to interact and collaborate in order to improve user request adaption tactics. Furthermore, a multi-agent DRL (Deep RL) algorithm is described that is based on the allocation of computational resources in such a way that overall energy consumption is minimized but latency requirements are always fulfilled. Finally, a federated approach based on DRL was created to reduce computational complexity and training excesses by requiring agents to communicate only the parameters of their models, rather than their full training data.  
A typical hierarchical architecture of a 6G network has been proposed in [64], a three-layer architecture: cloud intelligence, edge intelligence, and user intelligence.  
The user layer is made up of a large number of terminals that require computation from the edge intelligence layer or the deployment of numerous applications from the cloud intelligence layer. Smartphones, for example, rely on real-time computing services to handle more complicated tasks, such as language recognition, high-definition video, and 3D rendering. Automated robots rely on computing services in IoT industry (IIoT) networks to analyze production lines, and if necessary, change production strategy. For autonomous driving applications, a huge set of sensing data must be processed within very tight latency limits.  
Both cloud and the edge intelligence layers have computing and caching capabilities, allowing them to meet the needs of the user layer terminals. In general, the edge intelligence layer is used for services that require a modest amount of computing power, whereas the cloud intelligence layer is used for applications that demand a lot of processing power. Various network infrastructures, such as macro-base stations (MBSs), small cell base stations (SBSs), roadside units (RSUs), unmanned aerial vehicles (UAVs), and access points (APs), will be introduced in the edge network to give unlimited connectivity to a large number of terminals.  
These geographically distributed infrastructures, with computational skills, providing data storage and learning functions, will be fundamental for applications that require the use of artificial intelligence. The edge intelligence layer can be thought of as a collection of smart agents. Since the edge layer is so heterogeneous, each agent will be able to evolve independently and adapt its functions to the environment and users' requests. The proposed decentralized ML architecture integrates multiagent RL and FL into the edge network. In particular, autonomous agents can make local decisions on their own in the execution phase, based on their own observations of the surrounding environment, yet in the training phase, two collaborative schemes can be exploited, local information sharing and model parameter sharing. In the first, the agents investigate the environment by cooperating, allowing each individual agent to perfect their local strategies while learning more about the others. Model parameter sharing, on the other hand, involves agents training their own models independently, each based on their own observations, and then aggregating the data. This procedure can be performed locally by any agent or within the parameter server. The agents in the first scenario load their models into the PS and then receive the global model, whereas in the second situation, the model characteristics are transferred directly amongst the agents.