3 papers
eess.SP2026
Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods
Felix Krahmer, He Lyu, Rayan Saab +3
We study the quantization of real-valued bandlimited signals on graphs, focusing on low-bit representations. We propose iterative noise-shaping algorithms for quantization, includi…
cs.LG2024
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge
Jin Qian, Kaimin Wei, Yongdong Wu +3
Federated learning (FL) has emerged as a privacy-preserving machine learning approach where multiple parties share gradient information rather than original user data. Recent work…
cs.LG2024
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning
Huan Bao, Kaimin Wei, Yongdong Wu +2
A Model Inversion (MI) attack based on Generative Adversarial Networks (GAN) aims to recover the private training data from complex deep learning models by searching codes in the l…