Generative adversarial networks in time series: A systematic literature review
Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …
years. Their impact has been seen mainly in the computer vision field with realistic image …
Generative adversarial networks: A survey toward private and secure applications
Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …
computer vision and natural language processing, among others, due to its generative …
Fednest: Federated bilevel, minimax, and compositional optimization
DA Tarzanagh, M Li… - … on Machine Learning, 2022 - proceedings.mlr.press
Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …
single-level structure. However, many contemporary ML problems-including adversarial …
Federated learning of generative image priors for MRI reconstruction
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …
Fedgan-ids: Privacy-preserving ids using gan and federated learning
Federated Learning (FL) is a promising distributed training model that aims to minimize the
data sharing to enhance privacy and performance. FL requires sufficient and diverse training …
data sharing to enhance privacy and performance. FL requires sufficient and diverse training …
A novel federated learning scheme for generative adversarial networks
Generative adversarial networks (GANs) have been advancing and gaining tremendous
interests from both academia and industry. With the development of wireless technologies, a …
interests from both academia and industry. With the development of wireless technologies, a …
Solving a class of non-convex minimax optimization in federated learning
The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …
adversarial training and policy evaluation in reinforcement learning to AUROC …
Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges
New technological advancements in wireless networks have enlarged the number of
connected devices. The unprecedented surge of data volume in wireless systems …
connected devices. The unprecedented surge of data volume in wireless systems …
Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning
Federated learning (FL) aims to protect data privacy by enabling clients to build machine
learning models collaboratively without sharing their private data. Recent works …
learning models collaboratively without sharing their private data. Recent works …
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …
improve learning of generalizable synthesis models that reliably translate source-onto target …