# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Adaptive FL for the Resource-Constrained Networking Scenarios with Stringent Task Requirements  
The FL process is an iterative process wherein the FL devices at each iteration perform the training operation with their own data, transmit the parameter update to centralized servers for averaging purposes, and receive back the global updated model from the FL server. Since each iteration involves both communication and computation processes, it adds an extra burden to the resource-constrained nodes, especially wireless devices with limited energy, time, computation, and networking resources, i.e., vehicles. Additionally, various new services with stringent requirements in terms of latency, data rates, reliability, etc., can pose many new challenges for enabling the FL-based applications in resource-constrained wireless networks. With this in mind, the work done in [71] has proposed a resource-efficient adaptive FL process for the vehicular scenario.  
An edge-computing-enabled vehicular network is considered where individual vehicular users (VUs) \(m=1,\cdots,M\) are requesting the services with limited latency requirements. The VUs are employing the nearby edge servers for performing the partial-offloading-based task processing by offloading a certain portion of their tasks, i.e., \(\alpha_{m}\). The FL-based distributed learning approach is used to find the optimal offloading amount. The FL process is implemented over the joint air-ground network, where air-network-based high altitude platforms (HAPs) are employed as an FL server. Vehicles with their datasets with certain mobility patterns are training the local ML models for solving the computation offloading problem, i.e., finding the optimal amount of vehicular data to be offloaded at the edge computing servers. Given that the FL process is an iterative process where each FL iteration creates a more reliable FL model for optimizing the offloading process, each vehicle is tentative to participate in a maximum number of FL iterations. However, since each FL iteration adds cost and vehicles are bound to perform both FL and task processing operations within a limited time, it is important to select the optimal number of FL iterations performed by each vehicle. Performing less of a number of FL iterations can add additional costs in terms of energy and latency and can make the system unreliable. By taking this into account, a joint latency and energy minimization problem for both FL and the vehicular task processing phases can be formed in terms of a constrained nonlinear optimization problem and solved through various clustered and distributed approaches. Figure 6, provides the overview of the proposed adaptive FL process.  
The simulation results show that the proposed FL-based platform over the joint air-ground network can outperform the traditional FL methods in terms of latency, energy, service failures, etc. Given that the upcoming 6G network is expected to be highly automated, resource-constrained, densified with heterogeneous users and networking platforms, and a complex ecosystem of new services and applications, such adaptive FL strategies would be very useful.