| dc.description.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. [...] | en_US |
