9 papers
Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design
Mingyuan Fan, Cen Chen
The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device machine learning applications.…
Evaluating Interactive Reasoning in Large Language Models: A Hierarchical Benchmark with Executable Games
Mingyuan Fan, Weiguang Han, Daixin Wang +3
We introduce a multi-turn interactive framework for reasoning evaluation that treats reasoning as active evidence acquisition and belief updating. Wherein, LLMs receive only the ta…
When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewards
Mingyuan Fan, Weiguang Han, Daixin Wang +3
Reinforcement Learning with Verifiable Rewards (RLVR) is a central paradigm for turning large language models (LLMs) into reliable problem solvers, especially in logic-heavy domain…
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Mingyuan Fan, Cen Chen, Chengyu Wang +2
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks. Such attacks exploit clients' uploaded gradients to reconst…
Responsible Diffusion Models via Constraining Text Embeddings within Safe Regions
Zhiwen Li, Die Chen, Mingyuan Fan +4
The remarkable ability of diffusion models to generate high-fidelity images has led to their widespread adoption. However, concerns have also arisen regarding their potential to pr…
Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models
Die Chen, Zhiwen Li, Mingyuan Fan +4
Despite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which lead…