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

Machine learning (ML) models, at the core of Artificial intelligence (AI), are widely applied in our digital world. To build these models, huge amounts of data must be collected, and many assets must be protected under privacy law. Data privacy is a critical issue when training and testing ML models. For privacy concerns to be adequately addressed in today’s ML systems, there needs to be considered privacy gaps in ML, as trained ML models can be vulnerable to adversarial attacks. In this regard, federated learning and blockchain networks are the new paradigms that have emerged with the promise of privacy-preserving by design while utilizing ML models. The new paradigms have promising privacy-preserving potential; however, they neglect several fundamental privacy and security issues the fact that adversaries can exploit shared gradients and global parameters, and the parameter server may drop gradients that have been mistakenly or deliberately updated. Also, enough data is available to train models. The proposed research addresses privacy concerns in federated and distributed environments. This is a general overview to establish a privacy-preserving framework that can provide privacy in federated and blockchain-based networks while address to fundamental privacy and security issues.

The goal of data governance is to ensure the availability, usability, integrity, and security of data used in an organization.

This study, via some possible contributions, suggests an effective CTI and CTH framework to improve critical infrastructure security by creating a threat correlation engine, developing new CTI methods, and implementing a multi-view and multi-kernel deep learning system for CTH.