1 citations · 1 across the 4 of their papers we have counts for
4 papers
SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning
Borui Li, Li Yan, Jianmin Liu
Federated Learning (FL) enables collaborative model training on decentralized data but remains vulnerable to gradient leakage attacks that can reconstruct sensitive user informatio…
SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios
Borui Li, Li Yan, Junhao Han +2
Homomorphic Encryption (HE) prevails in securing Federated Learning (FL), but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model pa…
SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
Jianmin Liu, Li Yan, Borui Li +2
Federated fine-tuning is critical for improving the performance of large language models (LLMs) in handling domain-specific tasks while keeping training data decentralized and priv…
INK: Inheritable Natural Backdoor Attack Against Model Distillation
Xiaolei Liu, Ming Yi, Kangyi Ding +4
Deep learning models are vulnerable to backdoor attacks, where attackers inject malicious behavior through data poisoning and later exploit triggers to manipulate deployed models.…