Client Level Differential Privacy

Client-level differential privacy in federated learning aims to enhance the privacy of individual participants' data during collaborative model training by adding noise to local model updates before sharing them with a central server. Current research focuses on improving the accuracy of models trained under these privacy constraints, exploring techniques like sparsification, variance reduction, and adaptive client selection algorithms to mitigate the negative impact of noise. This area is crucial for enabling secure and privacy-preserving machine learning in sensitive domains like healthcare and finance, where data sharing is restricted but collaborative learning is highly desirable.

Papers

September 30, 2024

Federated Instruction Tuning of LLMs with Domain Coverage Augmentation
Zezhou Wang, Yaxin Du, Zhuzhong Qian, Siheng Chen
Medical LLM Instruction Augmentation Federated Instruction Tuning Semantic Coverage Domain Augmentation Client Level Differential Privacy

October 13, 2023

Near-optimal Differentially Private Client Selection in Federated Settings
Syed Eqbal Alam, Dhirendra Shukla, Shrisha Rao
Private Algorithm Federated Setting Differential Privacy Guarantee Information Transfer Local Computation Client Level Differential Privacy

September 7, 2023

Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy
Zikai Zhang, Rui Hu
Differential Privacy Privacy Guarantee Variance Reduction Privacy Analysis Byzantine Attack Byzantine Robust Federated Learning Client Level Differential Privacy

August 7, 2023

Binary Federated Learning with Client-Level Differential Privacy
Lumin Liu, Jun Zhang, Shenghui Song, Khaled B. Letaief
Differential Privacy Privacy Preserving Differential Privacy Guarantee Communication Efficient Federated Learning Client Level Differential Privacy

July 24, 2023

Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
Meirui Jiang, Yuan Zhong, Anjie Le, Xiaoxiao Li, Qi Dou
Differential Privacy Federated Learning Medical Imaging Client Level Differential Privacy AI Intermediary

June 14, 2023

Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor
Privacy Level Client Level Differential Privacy Federated Learning Convergence

May 1, 2023

Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
Yifan Shi, Kang Wei, Li Shen, Yingqi Liu, Xueqian Wang, Bo Yuan, Dacheng Tao
Better Generalization Differentially Private Differentially Private Federated Learning Perturbation Robustness Client Level Differential Privacy Return Landscape

February 15, 2022

Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy
Rui Hu, Yanmin Gong, Yuanxiong Guo
Differential Privacy Privacy Analysis Sparse Perturbation Differentially Private Federated Learning Client Level Differential Privacy