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Found 3 papers, 1 papers with code
Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation
In this paper, we design and develop Asyn2F, an Asynchronous Federated learning Framework with bidirectional model aggregation.
An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator
In this paper, we perform an empirical study on the impact of several loss functions on the performance of standard GAN models, Deep Convolutional Generative Adversarial Networks (DCGANs).
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency
However, its deployment in practice has been hurdled by two issues: the privacy of data that has to be aggregated centrally for model training and high communication overhead due to transmission of a large amount of data usually geographically distributed.
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