34 citations · 44 across the 11 of their papers we have counts for
4 papers · 1 filter
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
Jun Zhang, Jue Wang, Huan Li +3
Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it in…
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Tianqu Kang, Zixin Wang, Hengtao He +3
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigate…
Mode Connectivity and Data Heterogeneity of Federated Learning
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning (FL) enables multiple clients to train a model while keeping their data private collaboratively. Previous studies have shown that data heterogeneity between clie…
Binary Federated Learning with Client-Level Differential Privacy
Lumin Liu, Jun Zhang, Shenghui Song +1
Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL sys…