Theoretically Principled Federated Learning for Balancing Privacy and Utility

24 May 2023

Papers citing "Theoretically Principled Federated Learning for Balancing Privacy and Utility"

9 / 9 papers shown

Title
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning Mingcong Xu Xiaojin Zhang Wei Chen Hai Jin FedML 43 0 0 08 Mar 2025
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation HaoYang Li Wei Chen Xiaojin Zhang FedML 73 0 0 18 Dec 2024
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory Xiaojin Zhang Wei Chen FedML 31 0 0 23 Jul 2024
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning Xiaojin Zhang Mingcong Xu Wei Chen FedML 24 0 0 05 Jul 2024
Deciphering the Interplay between Local Differential Privacy, Average Bayesian Privacy, and Maximum Bayesian Privacy Xiaojin Zhang Yulin Fei Wei Chen 31 1 0 25 Mar 2024
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning Xiaojin Zhang Yan Kang Lixin Fan Kai Chen Qiang Yang FedML 13 6 0 28 May 2023
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion Xiaojin Zhang Kai Chen Qian Yang FedML 14 5 0 07 May 2023
A Game-theoretic Framework for Privacy-preserving Federated Learning Xiaojin Zhang Lixin Fan Si-Yi Wang Wenjie Li Kai Chen Qiang Yang FedML 21 4 0 11 Apr 2023
Privacy Against Statistical Inference Flavio du Pin Calmon N. Fawaz FedML 94 345 0 08 Oct 2012