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Motivated by federated learning, we consider the hub-and-spoke model of distributed optimization in which a central authority coordinates the computation of a solution among many agents while limiting communication.
Towards federated learning at scale: System design
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Advances and open problems in federated learning
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The numerical solution of parabolic and elliptic differential equations
D. W. Peaceman and Jr. H. H. Rachford · 1955
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Convex Analysis
R. T. Rockafellar · 1970
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Monotone operators and the proximal point algorithm
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Topics in metric fixed point theory
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Parallel and Distributed Computation: Numerical Methods
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Is local SGD better than minibatch SGD?
B. E. Woodworth, K. K. Patel, S. U. Stich, Z. Dai, B. Bullins, H. B. McMahan, O. Shamir, and N. Srebro · 2002
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Convex optimization
S. Boyd and L. Vandenberghe · 2004
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Variational Analysis
R.T. Rockafellar and R. J-B Wets · 2009
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Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2010
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Optimal distributed online prediction using mini-batches
O. Dekel, R. Gilad-Bachrach, O. Shamir, and L. Xiao · 2012
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S. Lacoste-Julien, M. Schmidt, and F. Bach · 2012
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Communication-efficient algorithms for statistical optimization
Y. Zhang, J. C. Duchi, and M. J. Wainwright · 2013
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Communication efficient distributed machine learning with the parameter server
M. Li, D. G. Andersen, A. J. Smola, and K. Yu · 2014
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Proximal algorithms
N. Parikh, S. Boyd, et al · 2014
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CVXPY: A Python-embedded modeling language for convex optimization
Convex analysis and monotone operator theory in Hilbert spaces
H. H. Bauschke and P. L. Combettes · 2017
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Tight global linear convergence rate bounds for Douglas-Rachford splitting
P. Giselsson · 2017
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Linear convergence and metric selection for Douglas-Rachford splitting and ADMM
P. Giselsson and S. Boyd · 2017
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Newton sketch: A linear-time optimization algorithm with linear-quadratic convergence
M. Pilanci and M. J. Wainwright · 2017
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Protection against reconstruction and its applications in private federated learning
A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers · 2018
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Monotone operator theory in convex optimization
P. L. Combettes · 2018
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S. Diamond and S. Boyd · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Arcas · 2016
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Iterative Hessian Sketch: Fast and accurate solution approximation for constrained least-squares
M. Pilanci and M. J. Wainwright · 2016
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Distributed coordinate descent method for learning with big data
P. Richtárik and M. Takáč · 2016
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Primer on monotone operator methods
E. K. Ryu and S.P. Boyd · 2016
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2018
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Federated learning: challenges, methods and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2019
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Douglas-Rachford splitting for the sum of a Lipschitz continuous and a strongly monotone operator
W. M. Moursi and L. Vandenberghe · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
M. J. Wainwright · 2019
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