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We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner.
Geometric approximation via coresets
Pankaj K Agarwal, Sariel Har-Peled, Kasturi R Varadarajan, et al · 2005
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Gradient flows: in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2005
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A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
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Barycenters in the wasserstein space
Martial Agueh and Guillaume Carlier · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Diffeomorphic density matching by optimal information transport
Martin Bauer, Sarang Joshi, and Klas Modin · 2015
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On the rate of convergence in wasserstein distance of the empirical measure
Nicolas Fournier and Arnaud Guillin · 2015
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Jeff M Phillips · 2016
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Optimal mass transport: Signal processing and machine-learning applications
Soheil Kolouri, Se Rim Park, Matthew Thorpe, Dejan Slepcev, and Gustavo K Rohde · 2017
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Transport based image morphing with intensity modulation
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Sebastian Claici, Aude Genevay, and Justin Solomon · 2018
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Learning wasserstein embeddings
Nicolas Courty, Remi Flamary, and Melanie Ducoffe · 2018
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On coresets for logistic regression
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, and David Woodruff · 2018
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Private nearest neighbors classification in federated databases
Phillipp Schoppmann, Adrià Gascón, and Borja Balle · 2018
Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolo Fusi · 2020
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Exploiting Shared Representations for Personalized Federated Learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
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Sketching data sets for large-scale learning: Keeping only what you need
Remi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, and Philip Schniter · 2021
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Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, K. A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecny, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2021
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Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
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Differentially private optimal transport: Application to domain adaptation
Nam Lê Tien, Amaury Habrard, and Marc Sebban · 2019
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Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
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Secure efficient federated knn for recommendation systems
Zhaorong Liu, Leye Wang, and Kai Chen · 2021
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A Field Guide to Federated Optimization
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, and Wennan Zhu · 2021
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Manifold interpolating optimal-transport flows for trajectory inference
Guillaume Huguet, Daniel Sumner Magruder, Alexander Tong, Oluwadamilola Fasina, Manik Kuchroo, Guy Wolf, and Smita Krishnaswamy · 2022
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Approximate k-nearest neighbor query over spatial data federation
Kaining Zhang, Yongxin Tong, Yexuan Shi, Yuxiang Zeng, Yi Xu, Lei Chen, Zimu Zhou, Ke Xu, Weifeng Lv, and Zhiming Zheng · 2023
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