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We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
An ADMM based framework for automl pipeline configuration
Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, and Alexander Gray · 1905
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 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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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Auto-weka: Automated selection and hyper-parameter optimization of classification algorithms
Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown · 2012
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OpenML: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
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Learning hyperparameter optimization initializations
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2015
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Taking the human out of the loop: A review of bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas · 2016
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Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
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Selecting near-optimal learners via incremental data allocation
Ashish Sabharwal, Horst Samulowitz, and Gerald Tesauro · 2016
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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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Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown · 2017
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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cpsgd: Communication-efficient and differentially-private distributed sgd
Learning rate adaptation for federated and differentially private learning
A. Koskela and A. Honkela · 2019
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Robust federated learning through representation matching and adaptive hyper-parameters
H. Mostafa · 2019
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Automated machine learning with monte-carlo tree search
Herilalaina Rakotoarison, Marc Schoenauer, and Michele Sebag · 2019
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Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W Seeger, Cedric Archambeau, and Rodolphe Jenatton · 2019
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Weight sharing for hyperparameter optimization in federated learning
Mikhail Khodak, Tian Li, Liam Li, M Balcan, Virginia Smith, and Ameet Talwalkar · 2020
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Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, and H Brendan Mcmahan · 2018
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Rbfopt: an open-source library for black-box optimization with costly function evaluations
A Costa and G Nannicini · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Joaquin Vanschoren · 2018
Cited alongside, same era.
Scalable gaussian process-based transfer surrogates for hyperparameter optimization
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2018
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Adaptive histogram-based gradient boosted trees for federated learning
Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, and Heiko Ludwig · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Mitigating bias in federated learning
Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig · 2020
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Tifl: A tier-based federated learning system
Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng · 2020
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Federated bayesian optimization via thompson sampling
Z. Dai, B.K.H. Low, and P. Jaillet · 2020
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Adaptive federated optimization
S.J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konecny, S. Kumar, and H.B. McMahan · 2020
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IBM Federated Learning: an enterprise framework white paper v0. 1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, et al · 2020
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Auto-sklearn 2.0: The next generation
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter · 2020
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Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, and Ameet Talwalkar · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2021
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