Xiao-Han Yuan

VQRC: Variational Quantum Residual Compensation for Deep Neural Network Compression

The continuous scaling of neural networks improves representation capacity but imposes prohibitive storage, memory bandwidth, and energy costs during deployment. To address the accuracy degradation inherent in aggressive low-bit model compression, this paper proposes Variational Quantum Residual Compensation (VQRC), a...

Personalized Federated Learning with Low-Rank Pruning-Based Adaptation for Non-IID Data

Personalized Federated Learning (pFL) has emerged as a promising paradigm, while existing approaches face 3 limitations: granularity mismatch, resource waste, and conflict aggregation. This paper presents a Personalized Federated Learning with Low-rank Pruning-based Adaptation (pFedLoPA) framework. Instead of balancing...

Addressing class imbalance in federated learning: A perspective of catastrophic forgetting