X. Deng, J. Zhu, X. Pei, L. Zhang, Z. Ling and K. Xue, "Flow Topology-Based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT Networks," in IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 684-696, March 2023, doi: 10.1109/TNSM.2022.3213807.
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Release time:2024-03-13 Journal:IEEE Transactions on Network and Service Management Abstract:Abstract— The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL is used to protect consumer data privacy. However, using FL in VEC will lead to expensive communication overheads, thereby occupying regular communication resources. In the traditional FL, the massive communication rounds before convergence lead to enormous communication costs. Further more, in each communication round, many clients upload large quantity model parameters to the parameter server in the uplink communication phase, which increases communication overheads. Moreover, a few straggler links and clients may prolong training time in each round, which will decrease the efficiency of FL and potentially increase the communication costs. In this work, we propose an efficient-communication approach, which consists of three parts, including “Customized”, “Partial”, and “Flexible”, known as FedCPF. FedCPF provides a customized local training strategy for vehicular clients to achieve convergence quickly through a constraint item within fewer communication rounds. Moreover, considering the uplink congestion, we introduce a partial client participation rule to avoid numerous vehicles uploading their updates simultaneously. Besides, regarding the diverse finishing time points of federated training, we present a flexible aggregation policy for valid updates by constraining the upload time. Experimental results show that FedCPF outperforms the traditional FedAVG algorithm in terms of testing accuracy and communication optimization in various FL settings. Compared with the baseline, FedCPF achieves efficient communication with faster convergence speed and improves test accuracy by 6.31% on average. In addition, the average communication optimization rate is improved by 2.15 times. Note:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49289.htm Translation or Not:no -
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20-Flow Topology-Based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT Networks.pdf
Pre One:S. Liu, J. Yu, X. Deng and S. Wan, "FedCPF: An Efficient-Communication Federated Learning Approach for Vehicular Edge Computing in 6G Communication Networks," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 1616-1629, Feb. 2022, doi: 10.1109/TITS.2021.3099368. (中科院 1区)
Next One:X. Deng, P. Jiang and X. Chen, "Multirelational Collaborative Filtering for Global Graph Neural Networks to Mine Evolutional Social Relations," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2022.3229400. (JCR 1区)
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