The Value of Collaboration in Convex Machine Learning with Differential Privacy

24 June 2019

Papers citing "The Value of Collaboration in Convex Machine Learning with Differential Privacy"

35 / 35 papers shown

Title
FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA Seanie Lee Sangwoo Park Dong Bok Lee Dominik Wagner Haebin Seong Tobias Bocklet Juho Lee Sung Ju Hwang FedML 12 0 0 19 May 2025
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Zehan Zhu Yan Huang Xin Wang Shouling Ji Jinming Xu 31 0 0 10 May 2025
Bipartite Randomized Response Mechanism for Local Differential Privacy Shun Zhang Hai Zhu Zhili Chen N. Xiong 41 0 0 29 Apr 2025
Review of Mathematical Optimization in Federated Learning Shusen Yang Fangyuan Zhao Zihao Zhou Liang Shi Xuebin Ren Zongben Xu FedML AI4CE 87 0 0 02 Dec 2024
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy Jiating Ma Yipeng Zhou Qi Li Quan Z. Sheng Laizhong Cui Jiangchuan Liu FedML 33 1 0 16 Aug 2024
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory Xiaojin Zhang Wei Chen FedML 39 0 0 23 Jul 2024
CURE: Privacy-Preserving Split Learning Done Right Halil Ibrahim Kanpak Aqsa Shabbir Esra Genç Alptekin Küpçü Sinem Sav 24 0 0 12 Jul 2024
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds Zehan Zhu Yan Huang Xin Wang Jinming Xu 51 0 0 04 May 2024
Improving LoRA in Privacy-preserving Federated Learning Youbang Sun Zitao Li Yaliang Li Bolin Ding 35 61 0 18 Mar 2024
How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study Natalija Mitic Apostolos Pyrgelis Sinem Sav FedML 60 1 0 25 Feb 2024
DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming Shuya Feng Meisam Mohammady Han Wang Xiaochen Li Zhan Qin Yuan Hong 29 7 0 07 Dec 2023
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality Ziqin Chen Yongqiang Wang 39 4 0 25 Jun 2023
Privacy Against Hypothesis-Testing Adversaries for Quantum Computing F. Farokhi 20 2 0 24 Feb 2023
User-Entity Differential Privacy in Learning Natural Language Models Phung Lai Nhathai Phan Tong Sun R. Jain Franck Dernoncourt Jiuxiang Gu Nikolaos Barmpalios FedML 38 0 0 01 Nov 2022
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering Rui Wang Xingkai Wang H. Chen Jérémie Decouchant S. Picek Zichen Liu K. Liang 38 1 0 22 Aug 2022
Differentially Private Vertical Federated Clustering Zitao Li Tianhao Wang Ninghui Li FedML 36 18 0 02 Aug 2022
Privacy-Preserving Federated Recurrent Neural Networks Sinem Sav Abdulrahman Diaa Apostolos Pyrgelis Jean-Philippe Bossuat Jean-Pierre Hubaux 20 7 0 28 Jul 2022
Hercules: Boosting the Performance of Privacy-preserving Federated Learning Guowen Xu Xingshuo Han Shengmin Xu Tianwei Zhang Hongwei Li Xinyi Huang R. Deng FedML 35 16 0 11 Jul 2022
Differentially Private Federated Learning via Reconfigurable Intelligent Surface Yuhan Yang Yong Zhou Youlong Wu Yuanming Shi 20 25 0 31 Mar 2022
Private Non-Convex Federated Learning Without a Trusted Server Andrew Lowy Ali Ghafelebashi Meisam Razaviyayn FedML 36 25 0 13 Mar 2022
PPA: Preference Profiling Attack Against Federated Learning Chunyi Zhou Yansong Gao Anmin Fu Kai Chen Zhiyang Dai Zhi-Li Zhang Minhui Xue Yuqing Zhang AAML 25 22 0 10 Feb 2022
Continual Learning with Differential Privacy Pradnya Desai Phung Lai Nhathai Phan My T. Thai 32 7 0 11 Oct 2021
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning M. Aghapour A. Ferdowsi Walid Saad FedML 16 1 0 06 Oct 2021
Utility Fairness for the Differentially Private Federated Learning S. Alvi Yi Hong S. Durrani FedML 16 8 0 11 Sep 2021
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy Yipeng Zhou Xuezheng Liu Yao Fu Di Wu Chao Li Shui Yu FedML 32 2 0 05 Jul 2021
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning Wenzhuo Yang Yipeng Zhou Maio Hu Di Wu J. Zheng Hui Wang Song Guo FedML 19 11 0 31 Jan 2021
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times Yao Fu Yipeng Zhou Di Wu Shui Yu Yonggang Wen Chao Li FedML 34 9 0 11 Jan 2021
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learning F. Farokhi 12 4 0 30 Nov 2020
When Machine Learning Meets Privacy: A Survey and Outlook B. Liu Ming Ding Sina shaham W. Rahayu F. Farokhi Zihuai Lin 20 282 0 24 Nov 2020
Mitigating Sybil Attacks on Differential Privacy based Federated Learning Yupeng Jiang Yong Li Yipeng Zhou Xi Zheng FedML AAML 29 15 0 20 Oct 2020
Non-Stochastic Private Function Evaluation F. Farokhi G. Nair 24 3 0 20 Oct 2020
The Cost of Privacy in Asynchronous Differentially-Private Machine Learning F. Farokhi Nan Wu David Smith M. Kâafar FedML 20 0 0 18 Mar 2020
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization Kang Wei Jun Li Ming Ding Chuan Ma Hang Su Bo-Wen Zhang H. Vincent Poor FedML 25 11 0 29 Feb 2020
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 32 1,567 0 01 Nov 2019
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 104 572 0 08 Dec 2012