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Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach
FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset.
Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges.
A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network
Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems.
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