Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis
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.
- W. Tong and P. Zhu, “6G: the next horizon: From connected people and things to connected intelligence,” Cambridge University Press, 2021.
- B. McMahan, E. Moore, D. Ramage, S. Hampson and B.Arcas, “Communication-efficient learning of deep networks from decentralized data,” PMLR, 2017.
- S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” in Cryptography Mailing list at https://metzdowd.com, 2019.
- F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks 20.1 (2008): 61-80.
- C. Dwork, “Differential privacy,” Automata, Languages and Programming: 33rd International Colloquium, ICALP, 2006.
- R. C. Geyer, T. Klein, and M. Nabi, “Differentially private federated learning: A client level perspective,” arXiv:1712.07557, 2017.
- A. Shamir, “How to share a secret,” Communications of the ACM, 1979.
- T. Hastie, J. Friedman and R. Tibshirani, “The elements of statistical learning: data mining, inference, and prediction,” springer, 2009.
- M. Shayan, C. Fung, C. J. M. Yoon and I. Beschastnikh, “Biscotti: A blockchain system for private and secure federated learning,” IEEE Transactions on Parallel and Distributed Systems, 2020.
- G. Zyskind and Oz Nathan, “Decentralizing privacy: Using blockchain to protect personal data,” IEEE Security and Privacy Workshops, 2015.
- W. Ye, X. An, X. Yan, M. Hamad and S. Steinhorst, “FLaaS6G: Federated Learning as a Service in 6G Using Distributed Data Management Architecture,” GLOBECOM, 2022.
- P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” Advances in Cryptology—EUROCRYPT, 1999.
- Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, 1988.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.