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Found 8 papers, 0 papers with code
RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation
We introduce a new gradient-based loss function that computes the magnitude and direction of change in received signal strength (RSS) values from a point within the environment.
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.
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number.
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing
This theoretical bound is decomposed into two components, including the variance of the global gradient and the quadratic bias that can be minimized by optimizing the communication resources, and quantization/noise parameters.
Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers
We also study the effect of min-max normalization of I/Q samples within each classifier's input on generalization accuracy over simulated datasets with SNRs other than training set's SNR and show an average of 108. 05% improvement when I/Q samples are normalized.
Game of GANs: Game-Theoretical Models for Generative Adversarial Networks
This paper reviews the literature on the game theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative model and improve the GAN's performance.
Towards Ubiquitous AI in 6G with Federated Learning
One of the key challenges is the difficulty to implement distributed AI across a massive number of heterogeneous devices.
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.
