5 citations · 21 across the 10 of their papers we have counts for
10 papers
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
Caspar Meijer, Jiyue Huang, Shreshtha Sharma +2
Federated learning (FL) for time series forecasting (TSF) enables clients with privacy-sensitive time series (TS) data to collaboratively learn accurate forecasting models, for exa…
Gradient Inversion of Federated Diffusion Models
Jiyue Huang, Chi Hong, Lydia Y. Chen +1
Diffusion models are becoming defector generative models, which generate exceptionally high-resolution image data. Training effective diffusion models require massive real data, wh…
SFDDM: Single-fold Distillation for Diffusion models
Chi Hong, Jiyue Huang, Robert Birke +3
While diffusion models effectively generate remarkable synthetic images, a key limitation is the inference inefficiency, requiring numerous sampling steps. To accelerate inference…
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Jin Xu, Chi Hong, Jiyue Huang +2
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to…
Fabricated Flips: Poisoning Federated Learning without Data
Jiyue Huang, Zilong Zhao, Lydia Y. Chen +1
Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decen…
MEGA: Model Stealing via Collaborative Generator-Substitute Networks
Chi Hong, Jiyue Huang, Lydia Y. Chen
Deep machine learning models are increasingly deployedin the wild for providing services to users. Adversaries maysteal the knowledge of these valuable models by trainingsubstitute…
Attacks and Defenses for Free-Riders in Multi-Discriminator GAN
Zilong Zhao, Jiyue Huang, Stefanie Roos +1
Generative Adversarial Networks (GANs) are increasingly adopted by the industry to synthesize realistic images. Due to data not being centrally available, Multi-Discriminator (MD)-…
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
Jiyue Huang, Chi Hong, Lydia Y. Chen +1
Shapley Value is commonly adopted to measure and incentivize client participation in federated learning. In this paper, we show -- theoretically and through simulations -- that Sha…
An Exploratory Analysis on Users' Contributions in Federated Learning
Jiyue Huang, Rania Talbi, Zilong Zhao +3
Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core pr…
Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding
Ying Shen, Qiang Zhang, Jin Zhang +3
Automatic text classification (TC) research can be used for real-world problems such as the classification of in-patient discharge summaries and medical text reports, which is bene…