5 citations · 12 across the 8 of their papers we have counts for
8 papers
On the Efficiency of Privacy Attacks in Federated Learning
Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang +2
Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy att…
Hierarchical Pruning of Deep Ensembles with Focal Diversity
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei +1
Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing…
Invisible Watermarking for Audio Generation Diffusion Models
Xirong Cao, Xiang Li, Divyesh Jadav +4
Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in…
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei +1
Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to…
Rethinking Learning Rate Tuning in the Era of Large Language Models
Hongpeng Jin, Wenqi Wei, Xuyu Wang +2
Large Language Models (LLMs) represent the recent success of deep learning in achieving remarkable human-like predictive performance. It has become a mainstream strategy to leverag…
Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study
Sanjana Vijay Ganesh, Yanzhao Wu, Gaowen Liu +2
Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame b…