4 citations · 5 across the 6 of their papers we have counts for
6 papers · 1 filter
Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
Wenjin Mo, Zhiyuan Li, Minghong Fang +1
Federated learning (FL) allows multiple clients to collaboratively train a global machine learning model with coordination from a central server, without needing to share their raw…
Provably Robust Federated Reinforcement Learning
Minghong Fang, Xilong Wang, Neil Zhenqiang Gong
Federated reinforcement learning (FRL) allows agents to jointly learn a global decision-making policy under the guidance of a central server. While FRL has advantages, its decentra…
LoBAM: LoRA-Based Backdoor Attack on Model Merging
Ming Yin, Jingyang Zhang, Jingwei Sun +3
Model merging is an emerging technique that integrates multiple models fine-tuned on different tasks to create a versatile model that excels in multiple domains. This scheme, in th…
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Yichang Xu, Ming Yin, Minghong Fang +1
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as cl…
Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
Yueqi Xie, Minghong Fang, Neil Zhenqiang Gong
Model poisoning attacks are critical security threats to Federated Learning (FL). Existing model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal effec…
Poisoning Federated Recommender Systems with Fake Users
Ming Yin, Yichang Xu, Minghong Fang +1
Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attac…