3 citations · 4 across the 4 of their papers we have counts for
5 papers
Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Chaoyu Zhang, Shanghao Shi, Heng Jin +3
Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly train diagnostic models without ex…
Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions
Yue Li, Sudip Bhujel, Cameron Lira +2
Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL so…
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
Zhengyuan Jiang, Xingyu Lyu, Shanghao Shi +5
Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have sho…
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks
Md Hasan Shahriar, Ning Wang, Naren Ramakrishnan +2
The exponential adoption of machine learning (ML) is propelling the world into a future of distributed and intelligent automation and data-driven solutions. However, the proliferat…
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction
Shanghao Shi, Ning Wang, Yang Xiao +4
Federated learning is known for its capability to safeguard the participants' data privacy. However, recently emerged model inversion attacks (MIAs) have shown that a malicious par…