Volume 6 , Issue 1 , PP: 50-62, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Z. Abualkishik 1 * , Rasha Almajed 2 , William Thompson 3
Doi: https://doi.org/10.54216/IJWAC.060105
As computational power has increased rapidly in recent years, deep learning techniques have found widespread use in wireless internet of things (IoT) networks, where they have shown remarkable results. In order to make the most of the data contained in graphs and their surrounding contexts, graph intelligence has seen extensive use in a wide variety of tailored wireless applications. However, the sensitive nature of client data poses serious challenges to conventional customization approaches, which depend on centralized graph learning on globe graphs. In this work, we introduce federated graph learning, dubbed FGL, that is capable of producing accurate personalization while still protecting clients' anonymity. To train graph intelligence models jointly based on distributed graphs inferred from local data, we employ a trustworthy model updating technique. In order to make use of graph knowledge beyond the scope of dynamic interplay, we present a trustworthy graph extension mechanism for incorporating high-level knowledge while yet maintaining confidentiality. Six customization datasets were used to show that with excellent trustworthy protection, FGL achieves 2.0% to 5.0% lower errors than the state-of-the-art federated customization approaches. For ethical and insightful personalization, FGL offers a potential path forward for mining distributed graph data.
Graph Intelligence , Graph Learning , Wireless Networks , Internet of Things (IoT)
[1] M. Lee, G. Yu, and H. Dai, “Decentralized Inference with Graph Neural Networks in Wireless Communication Systems,” IEEE Trans. Mob. Comput., 2021, doi: 10.1109/tmc.2021.3125793.
[2] S. He et al., “An Overview onmthe Application of Graph Neural Networks in Wireless Networks,” IEEE Open Journal of the Communications Society. 2021, doi: 10.1109/OJCOMS.2021.3128637.
[3] J. Skarding, B. Gabrys, and K. Musial, “Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3082932.
[4] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A Comprehensive Survey on Graph Neural Networks,” IEEE Trans. Neural Networks Learn. Syst., 2021, doi: 10.1109/TNNLS.2020.2978386.
[5] Z. Zhang, P. Cui, and W. Zhu, “Deep Learning on Graphs: A Survey,” IEEE Trans. Knowl. Data Eng., 2022, doi: 10.1109/TKDE.2020.2981333.
[6] S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: a comprehensive review,” Comput. Soc. Networks, 2019, doi: 10.1186/s40649-019-0069-y.
[7] P. Li et al., “IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning,” IEEE Trans. Netw. Sci. Eng., 2022, doi: 10.1109/TNSE.2022.3145572.
[8] M. Joseph, A. Roth, J. Ullman, and B. Waggoner, “Local differential privacy for evolving data,” J. Priv. Confidentiality, 2020, doi: 10.29012/jpc.718.
[9] D. Chai, L. Wang, K. Chen, and Q. Yang, “Secure Federated Matrix Factorization,” IEEE Intell. Syst., 2021, doi: 10.1109/MIS.2020.3014880.
[10] C. Wu, F. Wu, L. Lyu, T. Qi, Y. Huang, and X. Xie, “A federated graph neural network framework for privacy-preserving personalization,” Nat. Commun., vol. 13, no. 1, p. 3091, Dec. 2022, doi: 10.1038/s41467-022-30714-9.
[11] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling, “Modeling Relational Data with Graph Convolutional Networks,” 2018, doi: 10.1007/978-3-319-93417-4_38.
[12] T. Qi, F. Wu, C. Wu, Y. Huang, and X. Xie, “Privacy-preserving news recommendation model learning,” 2020, doi 10.18653/v1/2020.findings-emnlp.128.
[13] “Https: //grouplens.org/datasets/movielens/."https://grouplens.org/datasets/movielens/ (accessed Nov. 10, 2022).
[14] Https://github.com/fmonti/mgcnn, “https://github.com/fmonti/mgcnn.” https://github.com/fmonti/mgcnn (accessed Nov. 10, 2022).
[15] X. Wang, X. He, M. Wang, F. Feng, and T. S. Chua, “Neural graph collaborative filtering,” 2019, doi: 10.1145/3331184.3331267.
[16] V. Perifanis and P. S. Efraimidis, “Federated Neural Collaborative Filtering,” Knowledge-Based Syst., 2022, doi: 10.1016/j.knosys.2022.108441.
[17] P. Veličković, A. Casanova, P. Liò, G. Cucurull, A. Romero, and Y. Bengio, “Graph attention networks,” 2018, doi: 10.1007/978-3-031-01587-8_7.
[18] L. Ruiz, F. Gama, and A. Ribeiro, “Gated Graph Recurrent Neural Networks,” IEEE Trans. Signal Process., 2020, doi: 10.1109/TSP.2020.3033962.
[19] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” 2017.
[20] S. Yun, M. Jeong, R. Kim, J. Kang, and H. J. Kim, “Graph transformer networks,” 2019.
[21] H. Brendan McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” 2017.
[22] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “FedProx,” MLSys2020, 2018.
[23] S. Ji, S. Pan, G. Long, X. Li, J. Jiang, and Z. Huang, “Learning Private Neural Language Modeling with Attentive Aggregation,” 2019, doi: 10.1109/IJCNN.2019.8852464.
[24] M. G. Arivazhagan, V. Aggarwal, A. K. Singh, and S. Choudhary, “Federated Learning with Personalization Layers,” Dec. 2019, [Online]. Available: http://arxiv.org/abs/1912.00818.
[25]A. Li et al., “LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning,” 2021, doi: 10.1145/3453142.3492909.
