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

Globecom, pp. 56–61

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 co-ordination 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.

Authors 5

1. Wenxuan Ye

   Huawei Technologies (Germany) · Technical University of Munich

   Affiliation as printed

   Munich Research Center, Huawei Technologies Duesseldorf GmbH,Advanced Wireless Technology Laboratory

   Advanced Wireless Technology Laboratory, Munich Research Center, Huawei Technologies Duesseldorf GmbH

   TUM School of Computation, Information and Technology, Technical University of Munich

2. Chendi Qian Aachen Computer Science 6: Machine Learning and Reasoning

   RWTH Aachen University

   Affiliation as printed

   Computer Science 6: Machine Learning and Reasoning, RWTH Aachen

3. Xueli An

   Huawei Technologies (Germany)

   Affiliation as printed

   Munich Research Center, Huawei Technologies Duesseldorf GmbH,Advanced Wireless Technology Laboratory

   Advanced Wireless Technology Laboratory, Munich Research Center, Huawei Technologies Duesseldorf GmbH

4. Xueqiang Yan

   Huawei Technologies (China)

   Affiliation as printed

   Huawei Technologies Co., Ltd,Wirele;s Technology Lab, 2012 Laboratories

   Wirele

5. Georg Carle

   Huawei Technologies (Germany)

   Affiliation as printed

   Munich Research Center, Huawei Technologies Duesseldorf GmbH,Advanced Wireless Technology Laboratory

   Advanced Wireless Technology Laboratory, Munich Research Center, Huawei Technologies Duesseldorf GmbH