Abstract

The advent of 5th generation (5G) wireless networks and Internet of Things (IoT) has led to the development of the need for privacy-aware and precise traffic predictions in network slicing scenarios. Centralized machine learning models usually encounter various obstacles due to data privacy, high communication costs, and scalability concerns. In order to mitigate such problems, this paper presents a new method of Secure Uncertainty-Aware Federated Learning (Secure UA-FL), which utilizes Bayesian LSTM models in combination with Monte Carlo Dropout (MCD) technique to predict traffic uncertainty in distributed edge nodes. The model is based on the uncertainty-aware aggregation, which adjusts client weights adaptively during the training process, and uses Krum-based Byzantine defense approach for improved performance and security. The proposed model is tested on synthesized 5G traffic datasets corresponding to eMBB, URLLC, and mMTC 5G network slices. The Secure UA-FL framework provides a scalable, secure, and privacy-aware solution for traffic prediction in dynamic 5G network slicing scenarios.

Authors

Vishal Kumar, Vikas Maheshkar
Netaji Subhas University of Technology, India

Keywords

Federated Learning, 5G Network Slicing, Traffic Prediction, Bayesian LSTM, Byzantine Defense