226 citations · 817 across the 57 of their papers we have counts for
19 papers · 1 filter
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing
Jiawei Zhang, Zhongzhu Chen, Huan Zhang +2
Diffusion models have been leveraged to perform adversarial purification and thus provide both empirical and certified robustness for a standard model. On the other hand, different…
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
Yuhang Chen, Chaoyun Zhang, Minghua Ma +7
Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accuratel…
Evaluation and Optimization of Gradient Compression for Distributed Deep Learning
Lin Zhang, Longteng Zhang, Shaohuai Shi +2
To accelerate distributed training, many gradient compression methods have been proposed to alleviate the communication bottleneck in synchronous stochastic gradient descent (S-SGD…
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Indus…
Fairness in Federated Learning via Core-Stability
Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang +2
Federated learning provides an effective paradigm to jointly optimize a model benefited from rich distributed data while protecting data privacy. Nonetheless, the heterogeneity nat…
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin +6
Diffusion models have been recently employed to improve certified robustness through the process of denoising. However, the theoretical understanding of why diffusion models are ab…