6 papers
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…
Exploring Federated Pruning for Large Language Models
Pengxin Guo, Yinong Wang, Wei Li +4
LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require acce…
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…
A New Federated Learning Framework Against Gradient Inversion Attacks
Pengxin Guo, Shuang Zeng, Wenhao Chen +4
Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demon…
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
Shuang Zeng, Pengxin Guo, Shuai Wang +3
Federated Learning (FL) is a rising approach towards collaborative and privacy-preserving machine learning where large-scale medical datasets remain localized to each client. Howev…