4 citations · 5 across the 15 of their papers we have counts for
5 papers · 1 filter
From Bilinear to Linear: Differentially Private Federated LoRA via Low-Dimensional Parameterization
Lele Zheng, Ruijie Hu, Tao Zhang +2
Federated Low-Rank Adaptation (LoRA) provides an efficient solution for finetuning large language models across distributed and privacy-sensitive data. However, despite avoiding ra…
FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA
Lele Zheng, Ruijie Hu, Tao Zhang +2
Low-Rank Adaptation (LoRA) enables communication-efficient federated fine-tuning of pretrained language models. However, integrating differential privacy (DP) into federated LoRA r…
Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models
Zikai Zhang, Rui Hu, Olivera Kotevska +1
Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate…
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
Jiahao Xu, Rui Hu, Olivera Kotevska
Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. Howe…
Traceable Black-box Watermarks for Federated Learning
Jiahao Xu, Rui Hu, Olivera Kotevska +1
Due to the distributed nature of Federated Learning (FL) systems, each local client has access to the global model, which poses a critical risk of model leakage. Existing works hav…