Stochastic Channel-Based Federated Learning for Medical Data Privacy
Preserving

v1v2v3 (latest)

Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving

23 October 2019

ArXiv (abs)PDF HTML

Papers citing "Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving"

16 / 16 papers shown

Title
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
Applied Federated Learning: Improving Google Keyboard Query Suggestions Timothy Yang Galen Andrew Hubert Eichner Haicheng Sun Wei Li Nicholas Kong Daniel Ramage F. Beaufays FedML 87 626 0 07 Dec 2018
Federated Learning for Mobile Keyboard Prediction Andrew Straiton Hard Kanishka Rao Zhifeng Lin Swaroop Indra Ramaswamy Youjie Li S. Augenstein Alex Schwing M. Annavaram A. Avestimehr FedML 136 1,545 0 08 Nov 2018
How To Backdoor Federated Learning Eugene Bagdasaryan Andreas Veit Yiqing Hua D. Estrin Vitaly Shmatikov SILM FedML 97 1,922 0 02 Jul 2018
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 133 1,297 0 20 Dec 2017
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning Tien-Ju Yang Yu-hsin Chen Vivienne Sze 3DV 89 740 0 16 Nov 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 306 4,649 0 18 Oct 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 143 1,902 0 08 Oct 2016
AIDE: Fast and Communication Efficient Distributed Optimization Sashank J. Reddi Jakub Konecný Peter Richtárik Barnabás Póczós Alex Smola 58 150 0 24 Aug 2016
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures Hengyuan Hu Rui Peng Yu-Wing Tai Chi-Keung Tang 72 891 0 12 Jul 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
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
Distributed Optimization with Arbitrary Local Solvers Chenxin Ma Jakub Konecný Martin Jaggi Virginia Smith Michael I. Jordan Peter Richtárik Martin Takáč 93 197 0 13 Dec 2015
Federated Optimization:Distributed Optimization Beyond the Datacenter Jakub Konecný H. B. McMahan Daniel Ramage FedML 120 737 0 11 Nov 2015
Communication Efficient Distributed Optimization using an Approximate Newton-type Method Ohad Shamir Nathan Srebro Tong Zhang 95 555 0 30 Dec 2013
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 137 1,490 0 01 Dec 2009