Proposed Smart 5G Framework: Incorporating Federated Learning and Transfer Learning

This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL)
and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity, service rate,
privacy preservation, low latency, and energy consumption, in the selection of access networks. The proposed framework will tackle
existing challenges in wireless communication systems, such as mobility, limited bandwidth, energy constraints, and limited feedback
from a receiver to a transmitter. The secondary objective is to address privacy preservation and scalability concerns during user
authentication in 5G networks. The federated user authentication model leverages the privacy preservation benefits of FL and secure
aggregation protocols during model averaging. The research methodology consists of five stages: literature review, classification of
objectives, determination of state metrics, definition of evaluation functions, and selection of FL and RL techniques. The resulting
framework is expected to provide a robust, secure, and efficient solution for 5G networks, ensuring enhanced quality of service and
optimization