4 citations · 10 across the 15 of their papers we have counts for
10 papers · 1 filter
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.…
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…
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness
Mingyuan Fan, Xiaodan Li, Cen Chen +2
A prevailing belief in attack and defense community is that the higher flatness of adversarial examples enables their better cross-model transferability, leading to a growing inter…
On the Robustness of Split Learning against Adversarial Attacks
Mingyuan Fan, Cen Chen, Chengyu Wang +2
Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., s…
On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook
Mingyuan Fan, Chengyu Wang, Cen Chen +2
Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life. However, the practical implementations of…
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…
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
Mingyuan Fan, Yang Liu, Cen Chen +2
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker…
Transferable Adversarial Examples with Bayes Approach
Mingyuan Fan, Cen Chen, Wenmeng Zhou +1
The vulnerability of deep neural networks (DNNs) to black-box adversarial attacks is one of the most heated topics in trustworthy AI. In such attacks, the attackers operate without…
Case-Aware Adversarial Training
Mingyuan Fan, Yang Liu, Cen Chen
The neural network (NN) becomes one of the most heated type of models in various signal processing applications. However, NNs are extremely vulnerable to adversarial examples (AEs)…
Enhance transferability of adversarial examples with model architecture
Mingyuan Fan, Wenzhong Guo, Shengxing Yu +2
Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model…