4 papers · 1 filter
SAU: Sparsity-Aware Unlearning for LLMs via Gradient Masking and Importance Redistribution
Yuze Wang, Yujia Tong, Xuan Liu +1
Large Language Models (LLMs) inevitably memorize sensitive information during training, posing significant privacy risks. Machine unlearning has emerged as a promising solution to…
Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
Tian Zhang, Yujia Tong, Junhao Dong +3
The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearning in quantized models. However…
Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction
Yujia Tong, Jingling Yuan, Chuang Hu
With the development of mobile and edge computing, the demand for low-bit quantized models on edge devices is increasing to achieve efficient deployment. To enhance the performance…
Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels
Yujia Tong, Yuze Wang, Jingling Yuan +1
Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing ma…