Network slicing has emerged as a crucial technology in modern communication networks, especially in the context of 5G and beyond. Network slices offer virtualized and isolated segments tailored to the specific applications and services. However, the inherent isolation imposes security challenges, limiting the slice’s ability to learn from all data sources. This paper proposes EDgE, a novel solution leveraging federated learning (FL) to address these concerns. The proposed framework leverages FL to facilitate learning from diverse data sources, enabling each slice to conduct local machine learning (ML) model training with encrypted model updates shared among intra and inter-communication service providers (CSPs). The EDgE framework enables dynamic selection of parameters for learning and optimizing specific attributes based on the slice type and requirements. This ensures that each slice receives tailored optimization strategies depending on their type, maximizing their efficiency. While intra-CSP data sharing presents challenges, proposing inter-CSP slice data sharing introduces additional complexities. To enhance data security and privacy, the framework utilizes Blockchain as a distributed ledger, ensuring transparency, immutability, and trust among CSPs. The EDgE architecture is simulated using production-grade Network Slice Management (NSM) and Cloud Orchestrator on real-world 4G datasets and NSL-KDD intrusion detection datasets. Test results show that EDgE rapidly converges to high accuracy, ultimately achieving 97.6% across different scenarios, outperforming legacy systems and enhancing the security of the NSM ecosystem.

https://ieeexplore.ieee.org/document/10600703