4 papers
Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation
Ziyue Lin, Jiahe Hou, Hongyu Xia +6
We propose Decoupled Residual Denoising Diffusion models (DRDD) for unified and data-efficient image-to-image (I2I) translation. While diffusion models have advanced I2I translatio…
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
Pengxin Guo, Runxi Wang, Shuang Zeng +7
Federated Learning (FL) has emerged as a promising privacy-preserving collaborative model training paradigm without sharing raw data. However, recent studies have revealed that pri…
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models
Weiying Zheng, Ziyue Lin, Pengxin Guo +3
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in cross-modal understanding and generation by integrating visual and textual information. While instruction…
Selective Aggregation for Low-Rank Adaptation in Federated Learning
Pengxin Guo, Shuang Zeng, Yanran Wang +3
We investigate LoRA in federated learning through the lens of the asymmetry analysis of the learned and matrices. In doing so, we uncover that matrices are responsible…