36 citations · 40 across the 11 of their papers we have counts for
11 papers
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
Ljubomir Rokvic, Panayiotis Danassis, Boi Faltings
In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for exa…
Unraveling Misinformation Propagation in LLM Reasoning
Yiyang Feng, Yichen Wang, Shaobo Cui +3
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, positioning them as promising tools for supporting human problem-solving. However, what happens…
FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market
Zhuan Shi, Patrick Ohl, Boi Faltings
Federated learning (FL) allows machine learning models to be trained on distributed datasets without directly accessing local data. In FL markets, numerous Data Consumers compete t…
CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models
Shunchang Liu, Zhuan Shi, Lingjuan Lyu +2
Assessing whether AI-generated images are substantially similar to source works is a crucial step in resolving copyright disputes. In this paper, we propose CopyJudge, a novel auto…
Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
Zhuan Shi, Yifei Song, Xiaoli Tang +2
Generative art using Diffusion models has achieved remarkable performance in image generation and text-to-image tasks. However, the increasing demand for training data in generativ…
Nuance Matters: Probing Epistemic Consistency in Causal Reasoning
Shaobo Cui, Junyou Li, Luca Mouchel +2
To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating inte…