Decentralized Stochastic Optimization with Inherent Privacy Protection

8 May 2022

Papers citing "Decentralized Stochastic Optimization with Inherent Privacy Protection"

25 / 25 papers shown

Title
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning Bin Li Xiaoye Miao Yongheng Shang Xinkui Zhao AAML 79 0 0 08 Jan 2025
Tailoring Gradient Methods for Differentially-Private Distributed Optimization Yongqiang Wang A. Nedić 50 71 0 02 Feb 2022
Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs Huan Gao Yongqiang Wang FedML 26 16 0 02 Jan 2022
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey Zhenheng Tang Shaoshuai Shi Wei Wang Yue Liu Xiaowen Chu 54 49 0 10 Mar 2020
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 94 2,204 0 21 Jun 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 142 481 0 28 May 2019
Differentially Private Model Publishing for Deep Learning Lei Yu Ling Liu C. Pu Mehmet Emre Gursoy Stacey Truex FedML 61 265 0 03 Apr 2019
Privacy-Preserving Average Consensus via State Decomposition Yongqiang Wang 32 172 0 25 Feb 2019
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 79 510 0 01 Feb 2019
Efficient Deep Learning on Multi-Source Private Data Nicholas Hynes Raymond Cheng D. Song FedML 59 102 0 17 Jul 2018
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 171 397 0 08 Jun 2018
Distributed Stochastic Gradient Tracking Methods Shi Pu A. Nedić 64 291 0 25 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 142 1,474 0 10 May 2018
Secure and Privacy-Preserving Consensus Minghao Ruan Huan Gao Yongqiang Wang FedML 53 237 0 12 Jul 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Ce Zhang Huan Zhang Cho-Jui Hsieh Wei Zhang Ji Liu 50 1,227 0 25 May 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 70 521 0 13 Feb 2017
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes A. Nedić Alexander Olshevsky Wei Shi César A. Uribe 55 140 0 19 Sep 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 203 6,121 0 01 Jul 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 236 3,208 0 15 Jun 2016
Privacy Preservation in Distributed Subgradient Optimization Algorithms Youcheng Lou Lean Yu Shouyang Wang 21 76 0 30 Dec 2015
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization Amir Daneshmand F. Facchinei Vyacheslav Kungurtsev G. Scutari 64 60 0 16 Jul 2014
Differentially Private Distributed Optimization Zhenqi Huang Sayan Mitra Nitin H. Vaidya FedML 62 265 0 12 Jan 2014
Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization Pascal Bianchi J. Jakubowicz 103 255 0 13 Jul 2011
Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties Feng Yan S. Sundaram S. V. N. Vishwanathan Y. Qi 89 269 0 21 Jun 2010
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 126 1,487 0 01 Dec 2009