5 papers
FedCova: Robust Federated Covariance Learning Against Noisy Labels
Xiangyu Zhong, Xiaojun Yuan, Ying-Jun Angela Zhang
Noisy labels in distributed datasets induce severe local overfitting and consequently compromise the global model in federated learning (FL). Most existing solutions rely on select…
Training-Free Rate-Distortion-Perception Traversal With Diffusion
Yuhan Wang, Suzhi Bi, Ying-Jun Angela Zhang
The rate-distortion-perception (RDP) tradeoff characterizes the fundamental limits of lossy compression by jointly considering bitrate, reconstruction fidelity, and perceptual qual…
Task-Oriented Computation Offloading for Edge Inference: An Integrated Bayesian Optimization and Deep Reinforcement Learning Framework
Xian Li, Suzhi Bi, Ying-Jun Angela Zhang
Edge intelligence (EI) allows resource-constrained edge devices (EDs) to offload computation-intensive AI tasks (e.g., visual object detection) to edge servers (ESs) for fast execu…
Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model
Yuhan Wang, Suzhi Bi, Ying-Jun Angela Zhang +1
The distortion-perception (DP) tradeoff reveals a fundamental conflict between distortion metrics (e.g., MSE and PSNR) and perceptual quality. Recent research has increasingly conc…
Task-Oriented Lossy Compression with Data, Perception, and Classification Constraints
Yuhan Wang, Youlong Wu, Shuai Ma +1
By extracting task-relevant information while maximally compressing the input, the information bottleneck (IB) principle has provided a guideline for learning effective and robust…