11 citations · 14 across the 4 of their papers we have counts for
4 papers
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…
Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection
Lele Zheng, Bowen Deng, Tao Zhang +2
Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from i…
FedProc: Prototypical Contrastive Federated Learning on Non-IID data
Xutong Mu, Yulong Shen, Ke Cheng +4
Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of al…