4 citations · 6 across the 13 of their papers we have counts for
6 papers · 1 filter
Graph Federated Learning for Personalized Privacy Recommendation
Ce Na, Kai Yang, Dengzhao Fang +6
Federated recommendation systems (FedRecs) have gained significant attention for providing privacy-preserving recommendation services. However, existing FedRecs assume that all use…
BDFirewall: Towards Effective and Expeditiously Black-Box Backdoor Defense in MLaaS
Ye Li, Chengcheng Zhu, Yanchao Zhao +1
In this paper, we endeavor to address the challenges of backdoor attacks countermeasures in black-box scenarios, thereby fortifying the security of inference under MLaaS. We first…
Towards Generalized and Stealthy Watermarking for Generative Code Models
Haoxuan Li, Jiale Zhang, Xiaobing Sun +1
Generative code models (GCMs) significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models req…
SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning
Chengcheng Zhu, Ye Li, Bosen Rao +3
Federated Learning (FL) has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenge…
BDPFL: Backdoor Defense for Personalized Federated Learning via Explainable Distillation
Chengcheng Zhu, Jiale Zhang, Di Wu +1
Federated learning is a distributed learning paradigm that facilitates the collaborative training of a global model across multiple clients while preserving the privacy of local da…
Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
Jiale Zhang, Bosen Rao, Chengcheng Zhu +6
Graph Neural Networks (GNNs) have achieved remarkable performance through their message-passing mechanism. However, recent studies have highlighted the vulnerability of GNNs to bac…