Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis

TL;DR

This paper proposes a secure, decentralized federated learning architecture for 6G networks using DLT, GNN, and homomorphic encryption to enhance privacy, security, and system reliability, validated through simulations.

Contribution

It introduces a novel trusted architecture combining DLT, GNN, and homomorphic encryption to address security and decentralization challenges in 6G federated learning.

Findings

Improved anomaly detection accuracy

Enhanced global model performance

Secure and transparent data management

Abstract

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.