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Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy

26 September 2022

Papers citing "Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy"

43 / 43 papers shown

Title
On Lattices, Learning with Errors, Random Linear Codes, and Cryptography O. Regev LRM 107 1,079 0 08 Jan 2024
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning Zhifeng Jiang Peng Ye Shiqi He Wei Wang Ruichuan Chen Bo Li 79 2 0 05 Jan 2024
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning Yiping Ma Jess Woods Sebastian Angel Antigoni Polychroniadou T. Rabin FedML 54 53 0 19 Aug 2023
DHSA: Efficient Doubly Homomorphic Secure Aggregation for Cross-silo Federated Learning Zizhen Liu Si-Quan Chen Jing Ye Junfeng Fan Huawei Li Xiaowei Li FedML 51 15 0 15 Aug 2022
Pisces: Efficient Federated Learning via Guided Asynchronous Training Zhifeng Jiang Wei Wang Baochun Li Yue Liu FedML 58 25 0 18 Jun 2022
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning Tayyebeh Jahani-Nezhad M. Maddah-ali Songze Li Giuseppe Caire FedML 80 48 0 24 Mar 2022
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors Timothy Stevens Christian Skalka C. Vincent J. Ring Samuel Clark Joseph P. Near FedML 56 70 0 13 Dec 2021
Federated Learning Based on Dynamic Regularization D. A. E. Acar Yue Zhao Ramon Matas Navarro Matthew Mattina P. Whatmough Venkatesh Saligrama FedML 69 776 0 08 Nov 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 70 126 0 11 Oct 2021
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning Jinhyun So Chaoyang He Chien-Sheng Yang Songze Li Qian-long Yu Ramy E. Ali Başak Güler Salman Avestimehr FedML 108 176 0 29 Sep 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 273 419 0 14 Jul 2021
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale Fan Lai Yinwei Dai Sanjay Sri Vallabh Singapuram Jiachen Liu Xiangfeng Zhu H. Madhyastha Mosharaf Chowdhury FedML 106 201 0 24 May 2021
See through Gradients: Image Batch Recovery via GradInversion Hongxu Yin Arun Mallya Arash Vahdat J. Álvarez Jan Kautz Pavlo Molchanov FedML 86 473 0 15 Apr 2021
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications Matthias Paulik M. Seigel Henry Mason Dominic Telaar Joris Kluivers ... Dominic Hughes O. Javidbakht Fei Dong Rehan Rishi Stanley Hung FedML 249 127 0 16 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 83 240 0 12 Feb 2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat Jennifer Gillenwater Eric Xing Afshin Rostamizadeh FedML 101 112 0 11 Oct 2020
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning S. Kadhe Nived Rajaraman O. O. Koyluoglu Kannan Ramchandran FedML 79 162 0 23 Sep 2020
Training Production Language Models without Memorizing User Data Swaroop Indra Ramaswamy Om Thakkar Rajiv Mathews Galen Andrew H. B. McMahan Franccoise Beaufays FedML 67 92 0 21 Sep 2020
IBM Federated Learning: an Enterprise Framework White Paper V0.1 Heiko Ludwig Nathalie Baracaldo Gegi Thomas Yi Zhou Ali Anwar ... Sean Laguna Mikhail Yurochkin Mayank Agarwal Ebube Chuba Annie Abay FedML 180 163 0 22 Jul 2020
Privacy Amplification via Random Check-Ins Borja Balle Peter Kairouz H. B. McMahan Om Thakkar Abhradeep Thakurta FedML 75 72 0 13 Jul 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 103 1,232 0 31 Mar 2020
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection Ruixuan Liu Yang Cao Masatoshi Yoshikawa Hong Chen FedML 45 108 0 24 Mar 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 177 1,437 0 29 Feb 2020
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning Jinhyun So Başak Güler A. Avestimehr FedML 87 298 0 11 Feb 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 256 6,261 0 10 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 514 42,449 0 03 Dec 2019
Privacy-preserving Federated Brain Tumour Segmentation Wenqi Li Fausto Milletarì Daguang Xu Nicola Rieke Jonny Hancox ... Maximilian Baust Yan Cheng Sébastien Ourselin M. Jorge Cardoso Andrew Feng FedML 73 478 0 02 Oct 2019
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma Radu Soricut SSL AIMat 368 6,455 0 26 Sep 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 143 1,150 0 13 Sep 2019
Towards Federated Learning at Scale: System Design Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman ... H. B. McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander FedML 121 2,666 0 04 Feb 2019
A Hybrid Approach to Privacy-Preserving Federated Learning Stacey Truex Nathalie Baracaldo Ali Anwar Thomas Steinke Heiko Ludwig Rui Zhang Yi Zhou FedML 54 896 0 07 Dec 2018
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 147 1,421 0 03 Dec 2018
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems Yunseong Lee Alberto Scolari Byung-Gon Chun M. Santambrogio Markus Weimer Matteo Interlandi VLM 44 116 0 14 Oct 2018
Distributed Differential Privacy via Shuffling Albert Cheu Adam D. Smith Jonathan R. Ullman David Zeber M. Zhilyaev FedML 90 352 0 04 Aug 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 58 39 0 11 Jul 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos Basel Alomair 144 1,143 0 22 Feb 2018
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 261 4,135 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,130 0 01 Jul 2016
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 433 18,361 0 27 May 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Adding Gradient Noise Improves Learning for Very Deep Networks Arvind Neelakantan Luke Vilnis Quoc V. Le Ilya Sutskever Lukasz Kaiser Karol Kurach James Martens AI4CE ODL 83 545 0 21 Nov 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,386 0 04 Sep 2014