4 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…
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…