The deployment of generative adversarial networks (GANs) in wireless networks faces three key challenges of limited devices’ computational capability, scarce communication resources, and severe data privacy leakage. To address these issues, this paper proposes a new distributed framework for training GANs based on ensemble learning. First, multiple discriminators are trained at many devices using their local datasets. A generator is then trained at a central server by aggregating devices’ discriminators in an ensemble manner. The per-round training time is established. Finally, simulation results show that the proposed framework can simultaneously reduce the training time and improve the learning performance as compared with an existing framework.


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    Title :

    Ensemble-Based Distributed Learning for Generative Adversarial Networks


    Contributors:
    Liu, Chonghe (author) / Ren, Jinke (author) / Yu, Guanding (author)


    Publication date :

    2022-06-01


    Size :

    553447 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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