An Expectation-Maximization Perspective on Federated Learning

An Expectation-Maximization Perspective on Federated Learning

19 November 2021

Christos Louizos

Joseph B. Soriaga

Papers citing "An Expectation-Maximization Perspective on Federated Learning"

17 / 17 papers shown

Title
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat Jennifer Gillenwater Eric Xing Afshin Rostamizadeh FedML 93 110 0 11 Oct 2020
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Zhize Li Hongyan Bao Xiangliang Zhang Peter Richtárik ODL 69 128 0 25 Aug 2020
Federated Learning With Quantized Global Model Updates M. Amiri Deniz Gunduz Sanjeev R. Kulkarni H. Vincent Poor FedML 66 131 0 18 Jun 2020
Entropic gradient descent algorithms and wide flat minima Fabrizio Pittorino Carlo Lucibello Christoph Feinauer Gabriele Perugini Carlo Baldassi Elizaveta Demyanenko R. Zecchina ODL MLT 66 33 0 14 Jun 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 142 1,431 0 29 Feb 2020
Learned Threshold Pruning K. Azarian Yash Bhalgat Jinwon Lee Tijmen Blankevoort MQ 45 38 0 28 Feb 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 183 6,229 0 10 Dec 2019
Statistical Model Aggregation via Parameter Matching Mikhail Yurochkin Mayank Agarwal S. Ghosh Kristjan Greenewald T. Hoang FedML 39 31 0 01 Nov 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 135 1,143 0 13 Sep 2019
Lookahead Optimizer: k steps forward, 1 step back Michael Ruogu Zhang James Lucas Geoffrey E. Hinton Jimmy Ba ODL 118 728 0 19 Jul 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 58 1,353 0 07 Mar 2019
The State of Sparsity in Deep Neural Networks Trevor Gale Erich Elsen Sara Hooker 123 758 0 25 Feb 2019
On First-Order Meta-Learning Algorithms Alex Nichol Joshua Achiam John Schulman 221 2,229 0 08 Mar 2018
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes Erin Grant Chelsea Finn Sergey Levine Trevor Darrell Thomas Griffiths BDL 77 509 0 26 Jan 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 120 1,407 0 05 Dec 2017
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 72 525 0 26 Oct 2017
Learning Structured Sparsity in Deep Neural Networks W. Wen Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 150 2,337 0 12 Aug 2016