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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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Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

Feb 19, 2021
Filip Hanzely, Boxin Zhao, Mladen Kolar

* 41 pages, 4 figures 

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Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization

Feb 14, 2021
Mher Safaryan, Filip Hanzely, Peter Richtárik

* 59 pages, 5 figues, 6 tables 

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Local SGD: Unified Theory and New Efficient Methods

Nov 03, 2020
Eduard Gorbunov, Filip Hanzely, Peter Richtárik

* 79 pages, 6 figures, 7 tables 

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Lower Bounds and Optimal Algorithms for Personalized Federated Learning

Oct 05, 2020
Filip Hanzely, Slavom├şr Hanzely, Samuel Horv├íth, Peter Richt├írik

* NeurIPS 2020 

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Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

Aug 26, 2020
Filip Hanzely

* PhD thesis, 425 pages, 75 figures 

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Stochastic Subspace Cubic Newton Method

Feb 21, 2020
Filip Hanzely, Nikita Doikov, Peter Richtárik, Yurii Nesterov

* 29 pages, 5 figures, 1 table, 1 algorithm 

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Federated Learning of a Mixture of Global and Local Models

Feb 14, 2020
Filip Hanzely, Peter Richtárik

* 43 pages, 8 algorithms, 6 figures, 1 table (minor formatting changes compared to the previous version) 

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Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems

Feb 11, 2020
Filip Hanzely, Dmitry Kovalev, Peter Richtarik

* 30 pages, 8 figures 

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Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit

May 28, 2019
Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik

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One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods

May 27, 2019
Filip Hanzely, Peter Richtárik

* 56 pages, 6 figures, 3 tables 

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A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent

May 27, 2019
Eduard Gorbunov, Filip Hanzely, Peter Richtárik

* 38 pages, 4 figures, 2 tables 

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99% of Parallel Optimization is Inevitably a Waste of Time

Jan 27, 2019
Konstantin Mishchenko, Filip Hanzely, Peter Richtárik

* 32 pages, 6 algorithms, 7 theorems, 12 figures 

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A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion

Jan 27, 2019
Filip Hanzely, Jakub Kone─Źn├Ż, Nicolas Loizou, Peter Richt├írik, Dmitry Grishchenko

* NeurIPS 2018, Privacy Preserving Machine Learning Workshop (camera ready version). The full-length paper, which includes a number of additional algorithms and results (including proofs of statements and experiments), is available in arXiv:1706.07636 

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SEGA: Variance Reduction via Gradient Sketching

Oct 18, 2018
Filip Hanzely, Konstantin Mishchenko, Peter Richtarik

* Accepted to the NIPS conference 

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A Nonconvex Projection Method for Robust PCA

May 21, 2018
Aritra Dutta, Filip Hanzely, Peter Richtárik

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