2 citations · 3 across the 13 of their papers we have counts for
13 papers
EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation
Zhaomin Wu, Jiayi Li, Bingsheng He
Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on the eight-item Patient Health Questionn…
FWeb3: A Practical Incentive-Aware Federated Learning Framework
Peishen Yan, Shuang Liang, Yang Hua +9
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate con…
Adaptive primal dual hybrid gradient algorithms based on average spectrum for saddle point problems
Shengjie Xu, Bingsheng He
The primal dual hybrid gradient algorithm (PDHG), which is also known as the Arrow-Hurwicz method, is a fundamental algorithm for saddle point problems especially in imaging. It al…
ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data
Haodong Zhao, Jinming Hu, Zhaomin Wu +7
Federated Instruction Tuning (FIT) enables collaborative instruction tuning of large language models across multiple organizations (clients) in a cross-silo setting without requiri…
LLM DNA: Tracing Model Evolution via Functional Representations
Zhaomin Wu, Haodong Zhao, Ziyang Wang +3
The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relationships through fine-tuning, di…
Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
Haodong Zhao, Jidong Li, Zhaomin Wu +4
Understanding persuasion is critical for the safety and reliability of multi-agent systems built on large language models (LLMs). This paper studies persuasion dynamics by contrast…
Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
Zhaomin Wu, Mingzhe Du, See-Kiong Ng +1
Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is i…
WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos
Zhaomin Wu, Ziyang Wang, Bingsheng He
Relational databases are often fragmented across organizations, creating data silos that hinder distributed data management and mining. Collaborative learning (CL) -- techniques th…
Learning Relational Tabular Data without Shared Features
Zhaomin Wu, Shida Wang, Ziyang Wang +1
Learning relational tabular data has gained significant attention recently, but most studies focus on single tables, overlooking the potential of cross-table learning. Cross-table…
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…
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Zhaomin Wu, Junyi Hou, Yiqun Diao +1
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Among its variants, Vertical Federated Learn…
Federated Data-Efficient Instruction Tuning for Large Language Models
Zhen Qin, Zhaomin Wu, Bingsheng He +1
Instruction tuning is a crucial step in improving the responsiveness of pretrained large language models (LLMs) to human instructions. Federated learning (FL) helps to exploit the…
Model-based Large Language Model Customization as Service
Zhaomin Wu, Jizhou Guo, Junyi Hou +3
Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customiza…