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Found 5 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.
A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks
We adopt the Deep Q-Learning technique to train the DGCNN model in the DRL framework without the need for a labeled training dataset, enabling the framework to quickly adapt to traffic dynamics.
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.
Machine Learning-based Link Fault Identification and Localization in Complex Networks
In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e. g., a wireless node changes its access point.
