Time to Next Flow Classification in Mobile Networks with Federated Learning

Sammanfattning: Understanding traffic dynamics and user demand in a cellular network is essential for effective resource management, which in turn improves the network’s energy and cost efficiency. This thesis focuses on the task of classifying the time until the arrival of the next flow at a user level in a real network traffic data set. A range of machine learning and deep learning techniques are applied to this task, some of which are incorporated into a federated learning framework that ensures data privacy. The results demonstrate that the long short-term memory (LSTM) performs best on this task, although good performance can also be achieved with models of lower complexity. Furthermore, models developed through federated learning achieve comparable performance to those trained on centralised data.