The Digital Twin Network (DTN) holds immense potential in shaping future networks by seamlessly integrating physical networks and virtual representations. However, high-fidelity twin network modeling proves complicated and resource-intensive. To address this challenge, this paper proposes a federated generative model with multiple generators for DTN (FMG-DTN) modeling, which can generate high-fidelity data without increasing communication overhead. Firstly, generative learning is employed, enabling FMG to accurately synthesize data following the distribution of real data. Secondly, the model of FMG and its corresponding loss function are designed to mitigate the mode collapse problem in the presence of data heterogeneity by encouraging different generators to synthesize data with different categories. The mode collapse problem, referring to the generated data lacking diversity, can be exacerbated by data heterogene-ity. Finally, complexity analysis reveals that FMG requires a smaller communication overhead than a state-of-the-art method. Empirical results on a real-world traffic dataset demonstrate that the FMG can be trained to synthesize data accurately within a few training iterations, even under heterogeneous data settings. Moreover, the FMG model exhibits a higher diversity in the categories of synthesized samples compared to the state-of-the-art method.
Federated Generative Learning for Digital Twin Network Modeling
2024-06-24
901205 byte
Conference paper
Electronic Resource
English
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