DP-ADMM: ADMM-based Distributed Learning with Differential Privacy

30 August 2018

Yuanxiong Guo

Papers citing "DP-ADMM: ADMM-based Distributed Learning with Differential Privacy"

50 / 69 papers shown

Title
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems Patrick Hytla Tran T. A. Nghia Duy Nhat Phan Andrew Rice FedML 52 0 0 17 Mar 2025
Towards Trustworthy Federated Learning Alina Basharat Yijun Bian Ping Xu Z. Tian FedML 72 0 0 05 Mar 2025
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems Roie Reshef Kfir Y. Levy FedML 42 0 0 17 Jul 2024
The Privacy Power of Correlated Noise in Decentralized Learning Youssef Allouah Anastasia Koloskova Aymane El Firdoussi Martin Jaggi R. Guerraoui 41 4 0 02 May 2024
Privacy-Preserving Distributed Optimization and Learning Ziqin Chen Yongqiang Wang 35 2 0 29 Feb 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 50 18 0 09 Jan 2024
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions T.-H. Hubert Chan Hao Xie Mengshi Zhao 51 1 0 14 Dec 2023
Topology-Dependent Privacy Bound For Decentralized Federated Learning Qiongxiu Li Wenrui Yu Changlong Ji Richard Heusdens 37 3 0 13 Dec 2023
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification Yiwei Li Chien-Wei Huang Shuai Wang Chong-Yung Chi Tony Q.S. Quek FedML 35 1 0 30 Oct 2023
DPZero: Private Fine-Tuning of Language Models without Backpropagation Liang Zhang Bingcong Li K. K. Thekumparampil Sewoong Oh Niao He 33 11 0 14 Oct 2023
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Zhiqin Yang Yonggang Zhang Yuxiang Zheng Xinmei Tian Hao Peng Tongliang Liu Bo Han FedML 35 62 0 08 Oct 2023
On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning Haoxiang Ye He Zhu Qing Ling FedML 46 11 0 28 Aug 2023
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality Ziqin Chen Yongqiang Wang 39 4 0 25 Jun 2023
Private Networked Federated Learning for Nonsmooth Objectives Franccois Gauthier C. Gratton Naveen K. D. Venkategowda Stefan Werner FedML 22 0 0 24 Jun 2023
Distributed and Scalable Optimization for Robust Proton Treatment Planning A. Fu V. Taasti M. Zarepisheh 24 2 0 27 Apr 2023
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM Ziba Parsons Fei Dou Houyi Du Zheng Song Jin Lu 32 3 0 25 Apr 2023
Differentially Private Distributed Convex Optimization Minseok Ryu Kibaek Kim FedML 35 1 0 28 Feb 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning Edwige Cyffers A. Bellet D. Basu FedML 39 5 0 24 Feb 2023
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning Tomoya Murata Taiji Suzuki 35 9 0 08 Feb 2023
Differentially Private Decentralized Optimization with Relay Communication Luqing Wang Luyao Guo Shaofu Yang Xinli Shi 38 0 0 21 Dec 2022
Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation Jason Hughes Juntao Chen OT 21 1 0 30 Nov 2022
Synthetic Dataset Generation for Privacy-Preserving Machine Learning Efstathia Soufleri Gobinda Saha Kaushik Roy DD 29 2 0 06 Oct 2022
Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression Xinlin Leng Chenxu Li Weifeng Xu Yuyan Sun Hongtao Wang FedML 32 1 0 16 Sep 2022
Exact Penalty Method for Federated Learning Shenglong Zhou Geoffrey Ye Li FedML 38 0 0 23 Aug 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM Chulin Xie Pin-Yu Chen Qinbin Li Arash Nourian Ce Zhang Bo Li FedML 47 16 0 20 Jul 2022
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning Alberto Blanco-Justicia David Sánchez J. Domingo-Ferrer K. Muralidhar 11 59 0 09 Jun 2022
FedGiA: An Efficient Hybrid Algorithm for Federated Learning Shenglong Zhou Geoffrey Ye Li FedML 38 16 0 03 May 2022
Federated Stochastic Primal-dual Learning with Differential Privacy Yiwei Li Shuai Wang Tsung-Hui Chang Chong-Yung Chi FedML 25 7 0 26 Apr 2022
Federated Learning via Inexact ADMM Shenglong Zhou Geoffrey Ye Li FedML 26 59 0 22 Apr 2022
Private Non-Convex Federated Learning Without a Trusted Server Andrew Lowy Ali Ghafelebashi Meisam Razaviyayn FedML 38 25 0 13 Mar 2022
Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning Chuan Ma Jun Li Kang Wei Bo Liu Ming Ding Long Yuan Zhu Han H. Vincent Poor 64 43 0 18 Feb 2022
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Rui Hu Yanmin Gong Yuanxiong Guo FedML 32 65 0 15 Feb 2022
Efficient Privacy Preserving Logistic Regression for Horizontally Distributed Data G. Miao 20 0 0 05 Feb 2022
HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation Chance N. DeSmet D. Cook 28 0 0 13 Nov 2021
Communication-Efficient ADMM-based Federated Learning Shenglong Zhou Geoffrey Ye Li FedML 40 22 0 28 Oct 2021
Canoe : A System for Collaborative Learning for Neural Nets Harshit Daga Yiwen Chen Aastha Agrawal Ada Gavrilovska FedML 24 2 0 27 Aug 2021
Low-Latency Federated Learning over Wireless Channels with Differential Privacy Kang Wei Jun Li Chuan Ma Ming Ding Cailian Chen Shi Jin Zhu Han H. Vincent Poor FedML 40 73 0 20 Jun 2021
Differentially Private Federated Learning via Inexact ADMM Minseok Ryu Kibaek Kim FedML 41 15 0 11 Jun 2021
Multi-Party Dual Learning Maoguo Gong Yuan Gao Yu Xie A. K. Qin Ke Pan Yew-Soon Ong 14 9 0 14 Apr 2021
Fusion of Federated Learning and Industrial Internet of Things: A Survey S. Priya Praveen Kumar Viet Quoc Pham K. Dev Reddy Maddikunta Thippa Reddy Thien Huynh-The AI4CE 41 195 0 04 Jan 2021
Privacy-preserving Decentralized Aggregation for Federated Learning Beomyeol Jeon S. Ferdous Muntasir Raihan Rahman A. Walid FedML 33 52 0 13 Dec 2020
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learning F. Farokhi 33 4 0 30 Nov 2020
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks Minh N. H. Nguyen N. H. Tran Y. Tun Zhu Han Choong Seon Hong FedML 27 49 0 25 Nov 2020
Differentially Private ADMM Algorithms for Machine Learning Tao Xu Fanhua Shang Yuanyuan Liu Hongying Liu Longjie Shen Maoguo Gong 38 17 0 31 Oct 2020
Image Obfuscation for Privacy-Preserving Machine Learning Mathilde Raynal R. Achanta Mathias Humbert 43 13 0 20 Oct 2020
Latent Dirichlet Allocation Model Training with Differential Privacy Fangyuan Zhao Xuebin Ren Shusen Yang Qing Han Peng Zhao Xinyu Yang 35 28 0 09 Oct 2020
Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and Possibilities Javad Mohammadi J. Thornburg 18 5 0 21 Sep 2020
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning Rui Hu Yanmin Gong FedML 28 63 0 11 Sep 2020
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning Jiahao Ding Jingyi Wang Guannan Liang J. Bi Miao Pan 19 12 0 11 Aug 2020
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization Rui Hu Yanmin Gong Yuanxiong Guo FedML 34 55 0 01 Aug 2020