28 citations · 28 across the 5 of their papers we have counts for
5 papers
ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
Zirui Gong, Leo Yu Zhang, Yanjun Zhang +4
Federated Learning (FL) enables collaborative model training by sharing model updates instead of raw data, aiming to protect user privacy. However, recent studies reveal that these…
Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning
Zhihao Chen, Zirui Gong, Jianting Ning +2
Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its discrete, ranking-based update mec…
ConSeg: Contextual Backdoor Attack Against Semantic Segmentation
Bilal Hussain Abbasi, Zirui Gong, Yanjun Zhang +3
Despite significant advancements in computer vision, semantic segmentation models may be susceptible to backdoor attacks. These attacks, involving hidden triggers, aim to cause the…
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
Zirui Gong, Yanjun Zhang, Leo Yu Zhang +3
Federated Ranking Learning (FRL) is a state-of-the-art FL framework that stands out for its communication efficiency and resilience to poisoning attacks. It diverges from the tradi…
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification
Zirui Gong, Liyue Shen, Yanjun Zhang +4
The collaborative nature of federated learning (FL) poses a major threat in the form of manipulation of local training data and local updates, known as the Byzantine poisoning atta…