New paradigms of distributed AI for improving 5G-based network systems performance

Abstract

With the advent of 5G technology, there is an increasing need for efficient and effective machine learning techniques to support a wide range of applications, from smart cities to autonomous vehicles. The research question is whether distributed machine learning can provide a solution to the challenges of large-scale data processing, resource allocation, and privacy concerns in 5G networks. The thesis examines two main approaches to distributed machine learning: split learning and federated learning. Split learning enables the separation of model training and data storage between multiple devices, while federated learning allows for the training of a global model using decentralized data sources. The thesis investigates the performance of these approaches in terms of accuracy, communication overhead, and privacy preservation. The findings suggest that distributed machine learning can provide a viable solution to the challenges of 5G networks, with split learning and federated learning techniques showing promising results for spectral efficiency, resource allocation, and privacy preservation. The thesis concludes with a discussion of future research directions and potential applications of distributed machine learning in 5G networks. In this thesis, we investigate four case studies of both 5G network systems and LTE and Wifi (legacy parts). In chapter3, we implement an asynchronous federated learning model to predict the RSSI in robot localization indoor and outdoor environments. The proposed framework provides a good performance in terms of convergence, accuracy, and overhead reduction. In chapter4, we transfer the deployment of the asynchronous federated learning framework from the Wifi use case to a part of 5G networks (Network slicing), where we use the framework to predict the slice type for rapid and automated intelligent resource allocation. [...]