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cs.LG2025
Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm
Hongyang Li, Lincen Bai, Caesar Wu +3
We propose LQ-SGD (Low-Rank Quantized Stochastic Gradient Descent), an efficient communication gradient compression algorithm designed for distributed training. LQ-SGD further deve…
cs.LG2025
Adaptive Pruning with Module Robustness Sensitivity: Balancing Compression and Robustness
Lincen Bai, Hedi Tabia, Raúl Santos-RodrÃguez
Neural network pruning has traditionally focused on weight-based criteria to achieve model compression, frequently overlooking the crucial balance between adversarial robustness an…
cs.LG2025
A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks
Haoyang Li, Li Bai, Qingqing Ye +4
Model Inversion (MI) attacks, which reconstruct the training dataset of neural networks, pose significant privacy concerns in machine learning. Recent MI attacks have managed to re…