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Picture for Samuel Horvath

A Field Guide to Federated Optimization


Jul 14, 2021
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu


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FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout


Mar 01, 2021
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane


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Optimal Client Sampling for Federated Learning


Oct 26, 2020
Wenlin Chen, Samuel Horvath, Peter Richtarik

* 11 pages, 10 pages of Appendix, 6 Figures, 3 algorithms, code available: https://github.com/SamuelHorvath/FL-optimal-client-sampling 

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Natural Compression for Distributed Deep Learning


May 27, 2019
Samuel Horvath, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtarik

* 8 pages, 20 pages of Appendix, 6 Tables, 14 Figures, 1 Algorithm, 10 Theorems, 9 Lemmas, 5 Definitions 

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Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop


Jan 24, 2019
Dmitry Kovalev, Samuel Horvath, Peter Richtarik

* 14 pages, 2 algorithms, 9 lemmas, 2 theorems, 4 figures 

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