… shifted toward new paradigms such as federated learning (FL) and split learning (SL) to improve
the … Specifically, we focus on GANs (generative adversarial networks) and leverage their …

… However, the data owners need to participate in an additional FL training to train GANs in a
… GANs with FL for data augmentation, and propose a lightweight alternative that utilizes Split …

… The methodology adopted in this study involves the integration of SFL and GANs. SFL, a …
because the model architecture is split. SFL’s ability to split the network architecture between …

… To better safeguard the privacy of user data, traditional ML techniques have transitioned
toward new paradigms like federated learning (FL) and split learning (SL). However, existing …

… model splitting and assignment. To take advantage of FL and SL, we propose a hybrid federated
split … of FL and the flexible splitting of SL. To reduce the computational idleness in model …

… -preserving architecture, split learning has found widespread … clients in federated learning
retaining the whole model, split … to partial model outputs, split learning remains susceptible to …

… However, the data owners need to participate in an additional FL training to train GANs in a
… GANs with FL for data augmentation, and propose a lightweight alternative that utilizes Split …

… GANs. To address these challenges, this paper presents a novel federated learning framework
for GANs… MD-GAN relies on transporting gradient to train the split GAN and uses a single-…

Split Federated Learning (SFL) is an emerging edge-friendly version of Federated Learning
(FL), where clients process a small portion of the entire model. While SFL was considered to …

… Notably, during the federated GANs training, we have devised a client selection strategy
aimed at enhancing the overall performance of GANs models on a global scale. …