7 papers
Meta-FC: Meta-Learning with Feature Consistency for Robust and Generalizable Watermarking
Yuheng Li, Weitong Chen, Chengcheng Zhu +4
Deep learning-based watermarking has made remarkable progress in recent years. To achieve robustness against various distortions, current methods commonly adopt a training strategy…
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…
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…
Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
Ye Li, Yanchao Zhao, Chengcheng Zhu +1
Federated Learning (FL), a privacy-preserving decentralized machine learning framework, has been shown to be vulnerable to backdoor attacks. Current research primarily focuses on t…