19 citations · 51 across the 11 of their papers we have counts for
11 papers
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
Zhaomin Wu, Zhen Qin, Junyi Hou +4
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning…
LLM-PBE: Assessing Data Privacy in Large Language Models
Qinbin Li, Junyuan Hong, Chulin Xie +10
Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Their profound capabilities in…
MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs
Qian Wang, Tianyu Wang, Zhenheng Tang +4
LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on pre…
GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
Zhen Xiang, Linzhi Zheng, Yanjie Li +9
The rapid advancement of large language model (LLM) agents has raised new concerns regarding their safety and security. In this paper, we propose GuardAgent, the first guardrail ag…
Effective and Efficient Federated Tree Learning on Hybrid Data
Qinbin Li, Chulin Xie, Xiaojun Xu +5
Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, m…
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
Moming Duan, Qinbin Li, Linshan Jiang +1
Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to pa…
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
Xiaoyuan Liu, Tianneng Shi, Chulin Xie +9
Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use…
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Chulin Xie, Pin-Yu Chen, Qinbin Li +3
Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL…
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
Zhaomin Wu, Qinbin Li, Bingsheng He
Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where pa…
Model-Contrastive Federated Learning
Qinbin Li, Bingsheng He, Dawn Song
Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to ha…
Practical One-Shot Federated Learning for Cross-Silo Setting
Qinbin Li, Bingsheng He, Dawn Song
Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to co…