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We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients.
Verification of forecasts expressed in terms of probability
Glenn W Brier et al · 1950
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Introductory Lectures on Convex Optimization, in: Applied Optimization
Y. Nesterov · 2004
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
Max Welling and Yee Whye Teh · 2011
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Large Scale Distributed Deep Networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
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Asymptotically Exact, Embarrassingly Parallel MCMC
W. Neiswanger, C. Wang, and E. Xing · 2013
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Distributed Stochastic Gradient MCMC
Sungjin Ahn, Babak Shahbaba, and Max Welling · 2014
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Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily B. Fox, and Carlos Guestrin · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Scalable and Robust Bayesian Inference via the Median Posterior
S. Minsker, S. Srivastava, L. Lin, and D. B. Dunson · 2014
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Parallel MCMC with Generalized Elliptical Slice Sampling
R. Nishihara, I. Murray, and R. P. Adams · 2014
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A Complete Recipe for Stochastic Gradient MCMC
Yi-An Ma, Tianqi Chen, and Emily B. Fox · 2015
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Privacy-preserving Deep Learning
Reza Shokri and Vitaly Shmatikov · 2015
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Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
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Stochastic Gradient MCMC with Stale Gradients
Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, and Lawrence Carin · 2016
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Federated Learning of Deep Networks using Model Averaging
H. McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Consistency and Fluctuations for Stochastic Gradient Langevin Dynamics
Yee Whye Teh, Alexandre Thiery, and Sebastian Vollmer · 2016
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Exploration of the (Non-) Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics
Sebastian J. Vollmer, Konstantinos C. Zygalakis, and Yee Whye Teh · 2016
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Further and Stronger Analogy Between Sampling and Optimization: Langevin Monte Carlo and Gradient Descent
Arnak S. Dalalyan · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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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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Non-convex Learning via Stochastic Gradient Langevin Dynamics: a Nonasymptotic Analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics
Yuchen Zhang, Percy Liang, and Moses Charikar · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Underdamped Langevin MCMC: A non-asymptotic analysis
Xiang Cheng, Niladri S Chatterji, Peter L Bartlett, and Michael I Jordan · 2018
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Parallel and Distributed MCMC via Shepherding Distributions
Scaffold: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Fedsplit: An Algorithmic Framework for Fast Federated Optimization
Reese Pathaky and Martin J. Wainwright · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor · 2020
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Minibatch vs Local SGD for Heterogeneous Distributed Learning
Blake Woodworth, Kumar Kshitij Patel, and Nathan Srebro · 2020
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Federated meta-learning for fraudulent credit card detection
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Arkabandhu Chowdhury and Chris Jermaine · 2018
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Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing
Oren Mangoubi and Nisheeth K. Vishnoi · 2018
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User-friendly Guarantees for the Langevin Monte Carlo with Inaccurate Gradient
Arnak S Dalalyan and Avetik Karagulyan · 2019
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High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
Alain Durmus and Éric Moulines · 2019
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Analysis of Langevin Monte Carlo via Convex Optimization
Alain Durmus, Szymon Majewski, and Błażej Miasojedow · 2019
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On the Convergence of Local Descent Methods in Federated Learning
Farzin Haddadpour and Mehrdad Mahdavi · 2019
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First Analysis of Local GD on Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2019
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Wenbo Zheng, Lan Yan, Chao Gou, and Fei-Yue Wang · 2020
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Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, and Afshin Rostamizadeh · 2021
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Federated Learning under Arbitrary Communication Patterns
Dmitrii Avdyukhin and Shiva Prasad Kasiviswanathan · 2021
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FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
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Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
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FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
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Federated Stochastic Gradient Langevin Dynamics
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Personalized Federated Learning via Variational Bayesian Inference
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