A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency

22 January 2020

Papers citing "A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency"

23 / 23 papers shown

Title
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications David Byrd Antigoni Polychroniadou FedML 14 155 0 12 Oct 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 220 767 0 28 Sep 2019
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm Mateusz Buda Ashirbani Saha Maciej A. Mazurowski 78 265 0 09 Jun 2019
Towards Fair and Privacy-Preserving Federated Deep Models Lingjuan Lyu Jiangshan Yu Karthik Nandakumar Yitong Li Xingjun Ma Jiong Jin Han Yu Kee Siong Ng FedML 30 20 0 04 Jun 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 29 1,343 0 07 Mar 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 41 2,297 0 13 Feb 2019
Fixup Initialization: Residual Learning Without Normalization Hongyi Zhang Yann N. Dauphin Tengyu Ma ODL AI4CE 43 348 0 27 Jan 2019
Privacy-Preserving Distributed Deep Learning for Clinical Data Brett K. Beaulieu-Jones W. Yuan S. G. Finlayson Zhiwei Steven Wu OOD FedML 29 46 0 04 Dec 2018
Privacy-Preserving Deep Learning via Weight Transmission L. T. Phong T. Phuong FedML 29 87 0 10 Sep 2018
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy Zhezhi He Boqing Gong Deliang Fan 24 22 0 20 Jul 2018
Beyond Data and Model Parallelism for Deep Neural Networks Zhihao Jia Matei A. Zaharia A. Aiken GNN AI4CE 43 501 0 14 Jul 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 74 608 0 24 Feb 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 91 1,394 0 05 Dec 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 118 985 0 22 May 2017
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 43 1,009 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 115 6,049 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 169 17,235 0 17 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 688 192,638 0 10 Dec 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 604 76,547 0 18 May 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 168 149,474 0 22 Dec 2014
Deep learning with Elastic Averaging SGD Sixin Zhang A. Choromańska Yann LeCun FedML 36 609 0 20 Dec 2014
Caffe: Convolutional Architecture for Fast Feature Embedding Yangqing Jia Evan Shelhamer Jeff Donahue Sergey Karayev Jonathan Long Ross B. Girshick S. Guadarrama Trevor Darrell VLM BDL 3DV 118 14,703 0 20 Jun 2014
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition Jeff Donahue Yangqing Jia Oriol Vinyals Judy Hoffman Ning Zhang Eric Tzeng Trevor Darrell VLM ObjD 57 4,946 0 06 Oct 2013