Jul 3, 2023 · In this paper, we propose a BAGAN as machine learning model which has the ability to create data for minority classes, and a Bi-FedAvg model as a new approach.
Abstract. In Federated Learning (FL), a shared model is learned across dispersive clients each of which often has small and heterogeneous data.
In Federated Learning (FL), a shared model is learned across dispersive clients each of which often has small and heterogeneous data.
Missing: B2- | Show results with:B2-
Note that, in general, BMCoGAN maintains a good balance between the S and U accuracies. That is, besides achieving higher U accuracy than other bidirectional ...
Missing: FedGAN: | Show results with:FedGAN:
Jun 12, 2020 · We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data ...
Missing: B2- Balanced directional
The effectiveness of GANs in producing images ac- cording to a specific visual domain has shown potential in unsupervised domain adaptation.
In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment.
Missing: B2- directional
Apr 22, 2024 · ABSTRACT. Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked.
This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. Our survey aims to explore the ...
This directory contains source code for reproducing the differentially private federated GAN results presented in the paper Generative Models for Effective ML
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