%%% meta learning
@inproceedings{huang2019efficient, author = {Huang, Silu and Wang, Chi and Ding, Bolin and Chaudhuri, Surajit}, title = {Efficient Identification of Approximate Best Configuration of Training in Large Datasets}, year = {2019}, isbn = {978-1-57735-809-1}, publisher = {AAAI Press}, url = {https://doi.org/10.1609/aaai.v33i01.33013862}, doi = {10.1609/aaai.v33i01.33013862}, booktitle = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence}, articleno = {474}, numpages = {8}, location = {Honolulu, Hawaii, USA}, series = {AAAI'19/IAAI'19/EAAI'19} }

@inproceedings{li2017hyperband,
  title={Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization.},
  author={Li, Lisha and Jamieson, Kevin G and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
  booktitle={ICLR (Poster)},
  pages={53},
  year={2017}
}

@inproceedings{jamieson2016non,
  title={Non-stochastic best arm identification and hyperparameter optimization},
  author={Jamieson, Kevin and Talwalkar, Ameet},
  booktitle={Artificial intelligence and statistics},
  pages={240--248},
  year={2016},
  organization={PMLR}
}

@article{JMLR:v23:21-0888,
  author  = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter},
  title   = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {54},
  pages   = {1--9},
  url     = {http://jmlr.org/papers/v23/21-0888.html}
}

@article{klein2017fast,
  title={Fast bayesian hyperparameter optimization on large datasets},
  author={Klein, Aaron and Falkner, Stefan and Bartels, Simon and Hennig, Philipp and Hutter, Frank},
  journal={Electronic Journal of Statistics},
  volume={11},
  number={2},
  pages={4945--4968},
  year={2017},
  publisher={Institute of Mathematical Statistics and Bernoulli Society}
}


@inproceedings{falkner2018bohb,
  title={BOHB: Robust and efficient hyperparameter optimization at scale},
  author={Falkner, Stefan and Klein, Aaron and Hutter, Frank},
  booktitle={International Conference on Machine Learning},
  pages={1437--1446},
  year={2018},
  organization={PMLR}
}

@inproceedings{efimova2017fast,
  title={Fast automated selection of learning algorithm and its hyperparameters by reinforcement learning},
  author={Efimova, Valeria and Filchenkov, Andrey and Shalamov, Viacheslav},
  booktitle={International Conference on Machine Learning AutoML Workshop},
  year={2017}
}

@article{shahriari2015taking,
  title={Taking the human out of the loop: A review of Bayesian optimization},
  author={Shahriari, Bobak and Swersky, Kevin and Wang, Ziyu and Adams, Ryan P and De Freitas, Nando},
  journal={Proceedings of the IEEE},
  volume={104},
  number={1},
  pages={148--175},
  year={2015},
  publisher={IEEE}
}

@inproceedings{olson2016tpot,
  title={TPOT: A tree-based pipeline optimization tool for automating machine learning},
  author={Olson, Randal S and Moore, Jason H},
  booktitle={Workshop on automatic machine learning},
  pages={66--74},
  year={2016},
  organization={PMLR}
}


@inproceedings{li2020efficient,
  title={{Efficient Automatic CASH via Rising Bandits}},
  author={Li, Yang and Jiang, Jiawei and Gao, Jinyang and Shao, Yingxia and Zhang, Ce and Cui, Bin},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  pages={4763--4771},
  year={2020}
}


@article{li2020federated,
  title={Federated learning: Challenges, methods, and future directions},
  author={Li, Tian and Sahu, Anit Kumar and Talwalkar, Ameet and Smith, Virginia},
  journal={IEEE Signal Processing Magazine},
  volume={37},
  number={3},
  pages={50--60},
  year={2020},
  publisher={IEEE}
}

@inproceedings{li2020rising,
  author    = {Yang Li and
               Jiawei Jiang and
               Jinyang Gao and
               Yingxia Shao and
               Ce Zhang and
               Bin Cui},
  title     = {Efficient Automatic {CASH} via Rising Bandits},
  booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
               2020, The Thirty-Second Innovative Applications of Artificial Intelligence
               Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
               Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
               February 7-12, 2020},
  pages     = {4763--4771},
  publisher = {{AAAI} Press},
  year      = {2020},
  url       = {https://ojs.aaai.org/index.php/AAAI/article/view/5910},
  timestamp = {Tue, 29 Mar 2022 16:01:26 +0200},
  biburl    = {https://dblp.org/rec/conf/aaai/LiJGSZ020.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{finn2019online,
  title={Online Meta-Learning},
  author={Finn, Chelsea and Rajeswaran, Aravind and Kakade, Sham and Levine, Sergey},
  journal={arXiv preprint arXiv:1902.08438},
  year={2019}
}

@article{rbfopt,
  title={RBFOpt: an open-source library for black-box optimization with costly function evaluations},
  author={Costa, A and Nannicini, G},
  journal={Math. Prog. Comp. 10},
  year={2018}
}

@article{finn2017model,
  title={Model-agnostic meta-learning for fast adaptation of deep networks},
  author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  journal={arXiv preprint arXiv:1703.03400},
  year={2017}
}


@article{wistuba2018scalable,
  title={Scalable gaussian process-based transfer surrogates for hyperparameter optimization},
  author={Wistuba, Martin and Schilling, Nicolas and Schmidt-Thieme, Lars},
  journal={Machine Learning},
  volume={107},
  number={1},
  pages={43--78},
  year={2018},
  publisher={Springer}
}
@inproceedings{wistuba2020few,
  title={Few-Shot Bayesian Optimization with Deep Kernel Surrogates},
  author={Wistuba, Martin and Grabocka, Josif},
  booktitle={International Conference on Learning Representations},
  year={2020}
}

@inproceedings{Reddi20Adaptive,
  title={Adaptive Federated Optimization},
  author={Reddi, S.J. and Charles, Z. and Zaheer, M. and Garrett, Z. and Rush, K. and Konecny, J. and Kumar, S. and McMahan, H.B.},
  booktitle={International Conference on Learning Representations},
  year={2020}
}




@inproceedings{wistuba2015hyperparameter,
  title={Hyperparameter search space pruning--a new component for sequential model-based hyperparameter optimization},
  author={Wistuba, Martin and Schilling, Nicolas and Schmidt-Thieme, Lars},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={104--119},
  year={2015},
  organization={Springer}
}
@inproceedings{wistuba2015learningHPOinits,
  title={Learning hyperparameter optimization initializations},
  author={Wistuba, Martin and Schilling, Nicolas and Schmidt-Thieme, Lars},
  booktitle={2015 IEEE international conference on data science and advanced analytics (DSAA)},
  pages={1--10},
  year={2015},
  organization={IEEE}
}

@article{Dai20FBO,
  title={Federated Bayesian Optimization via Thompson Sampling},
  author={Dai, Z. and Low, B.K.H. and Jaillet, P.},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

@article{Dai21Differentially,
  title={Differentially Private Federated Bayesian Optimization with Distributed Exploration},
  author={Dai, Z. and Low, B.K.H. and Jaillet, P.},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}


@article{perrone2019learning,
  title={Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning},
  author={Perrone, Valerio and Shen, Huibin and Seeger, Matthias W and Archambeau, Cedric and Jenatton, Rodolphe},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  pages={12771--12781},
  year={2019}
}
@inproceedings{wistuba2015learningDatasetSimilarities,
  title={Learning Data Set Similarities for Hyperparameter Optimization Initializations.},
  author={Wistuba, Martin and Schilling, Nicolas and Schmidt-Thieme, Lars},
  booktitle={Metasel@ pkdd/ecml},
  pages={15--26},
  year={2015}
}

@inproceedings{feurer2015initializing,
  title={Initializing bayesian hyperparameter optimization via meta-learning},
  author={Feurer, Matthias and Springenberg, Jost and Hutter, Frank},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={29},
  number={1},
  year={2015}
}



@article{vanschoren2018meta,
  title={Meta-learning: A survey},
  author={Vanschoren, Joaquin},
  journal={arXiv preprint arXiv:1810.03548},
  year={2018}
}

@article{Mostafa19Robust,
  title={Robust federated learning through representation matching and adaptive hyper-parameters},
  author={Mostafa, H.},
  journal={arXiv preprint arXiv:1912.13075},
  year={2019}
}

@article{Koskela19Learning,
  title={Learning rate adaptation for federated and differentially private learning},
  author={Koskela, A. and Honkela, A.},
  journal={arXiv preprint arXiv:1809.03832},
  year={2019}
}


@article{nichol2018first,
  title={On first-order meta-learning algorithms},
  author={Nichol, Alex and Achiam, Joshua and Schulman, John},
  journal={arXiv preprint arXiv:1803.02999},
  year={2018}
}

@article{fallah2020personalized,
  title={Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach},
  author={Fallah, Alireza and Mokhtari, Aryan and Ozdaglar, Asuman},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={3557--3568},
  year={2020}
}


%% FL-HPO
@article{khodak2021federated,
  title={Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing},
  author={Khodak, Mikhail and Tu, Renbo and Li, Tian and Li, Liam and Balcan, Maria-Florina and Smith, Virginia and Talwalkar, Ameet},
  journal={arXiv preprint arXiv:2106.04502},
  year={2021}
}

@inproceedings{khodak2020weight,
  title={Weight sharing for hyperparameter optimization in federated learning},
  author={Khodak, Mikhail and Li, Tian and Li, Liam and Balcan, M and Smith, Virginia and Talwalkar, Ameet},
  booktitle={Int. Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020},
  year={2020}
}

%% OpenML
@article{OpenML2013,
  author = {Vanschoren, Joaquin and van Rijn, Jan N. and Bischl, Bernd and Torgo, Luis},
  title = {{OpenML}: Networked Science in Machine Learning},
  journal = {SIGKDD Explorations},
  volume = {15},
  number = {2},
  year = {2013},
  pages = {49--60},
  url = {http://doi.acm.org/10.1145/2641190.2641198},
  doi = {10.1145/2641190.2641198},
  publisher = {ACM},
  address = {New York, NY, USA},
}

@article{feurer2021openml,
  title={{OpenML}-{Python}: An extensible {Python API for OpenML}},
  author={Feurer, Matthias and van Rijn, Jan N and Kadra, Arlind and Gijsbers, Pieter and Mallik, Neeratyoy and Ravi, Sahithya and M{\"u}ller, Andreas and Vanschoren, Joaquin and Hutter, Frank},
  journal={Journal of Machine Learning Research},
  volume={22},
  number={100},
  pages={1--5},
  year={2021}
}


%% Gradient boosting papers
@article{friedman2001greedy,
  title={Greedy function approximation: a gradient boosting machine},
  author={Friedman, Jerome H},
  journal={Annals of statistics},
  pages={1189--1232},
  year={2001},
  publisher={JSTOR}
}

@article{ke2017lightgbm,
  title={Lightgbm: A highly efficient gradient boosting decision tree},
  author={Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
  journal={Advances in neural information processing systems},
  volume={30},
  pages={3146--3154},
  year={2017}
}

@inproceedings{xgboost,
 author = {Chen, Tianqi and Guestrin, Carlos},
 title = {XGBoost: A Scalable Tree Boosting System},
 booktitle = {Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '16},
 year = {2016},
 isbn = {978-1-4503-4232-2},
 location = {San Francisco, California, USA},
 pages = {785--794},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2939672.2939785},
 doi = {10.1145/2939672.2939785},
 acmid = {2939785},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {large-scale machine learning},
} 

%% Federated boosting
@article{ong2020adaptive,
  title={Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning},
  author={Ong, Yuya Jeremy and Zhou, Yi and Baracaldo, Nathalie and Ludwig, Heiko},
  journal={arXiv preprint arXiv:2012.06670},
  year={2020}
}

@inproceedings{li2020practical,
  title={Practical federated gradient boosting decision trees},
  author={Li, Qinbin and Wen, Zeyi and He, Bingsheng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={4642--4649},
  year={2020}
}


%% Scikit learn
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

%% HPO
@article{shahriari2016taking,
  title={Taking the human out of the loop: A review of bayesian optimization},
  author={Shahriari, B. and Swersky, K. and Wang, Z. and Adams, R. P. and De Freitas, N.},
  journal={Proceedings of the IEEE},
  volume={104},
  number={1},
  pages={148--175},
  year={2016},
  publisher={IEEE}
}

@article{li2018hyperband,
  title={Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization},
  author={Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
  journal={Journal of Machine Learning Research},
  volume={18},
  number={185},
  pages={1--52},
  year={2018}
}


@inproceedings{hutter2011sequential,
  title={Sequential model-based optimization for general algorithm configuration},
  author={Hutter, Frank and Hoos, Holger H and Leyton-Brown, Kevin},
  booktitle={International Conference on Learning and Intelligent Optimization},
  pages={507--523},
  year={2011},
  organization={Springer}
}


@inproceedings{bergstra2011algorithms,
  title={Algorithms for hyper-parameter optimization},
  author={Bergstra, James S and Bardenet, R{\'e}mi and Bengio, Yoshua and K{\'e}gl, Bal{\'a}zs},
  booktitle={Advances in neural information processing systems},
  pages={2546--2554},
  year={2011}
}

@inproceedings{komer2014hyperopt,
  title={Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn},
  author={Komer, Brent and Bergstra, James and Eliasmith, Chris},
  booktitle={ICML workshop on AutoML},
  pages={2825--2830},
  year={2014},
  organization={Citeseer}
}

@InProceedings{bohb,
  title = 	 {{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},
  author = 	 {Falkner, Stefan and Klein, Aaron and Hutter, Frank},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {1437--1446},
  year = 	 {2018},
}


%% CASH
@inproceedings{liu2020admm,
    title={An {ADMM} Based Framework for AutoML Pipeline Configuration},
    author={Sijia Liu and Parikshit Ram and Deepak Vijaykeerthy and Djallel Bouneffouf and Gregory Bramble and Horst Samulowitz and Dakuo Wang and Andrew Conn and Alexander Gray},
    booktitle={Thirty-Fourth AAAI Conference on Artificial Intelligence},
    year={2020},
    url={https://arxiv.org/abs/1905.00424v5},
}
@inproceedings{rakotoarison2019automated,
  title     = {Automated Machine Learning with Monte-Carlo Tree Search},
  author    = {Rakotoarison, Herilalaina and Schoenauer, Marc and Sebag, Michele},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  pages     = {3296--3303},
  year      = {2019},
}

@article{mohr2018ml,
  title={ML-Plan: Automated machine learning via hierarchical planning},
  author={Mohr, Felix and Wever, Marcel and H{\"u}llermeier, Eyke},
  journal={Machine Learning},
  volume={107},
  number={8-10},
  pages={1495--1515},
  year={2018},
  publisher={Springer}
}

@article{autoweka2,
 author = {Kotthoff, Lars and Thornton, Chris and Hoos, Holger H. and Hutter, Frank and Leyton-Brown, Kevin},
 title = {Auto-WEKA 2.0: Automatic Model Selection and Hyperparameter Optimization in WEKA},
 journal = {J. Mach. Learn. Res.},
 issue_date = {January 2017},
 volume = {18},
 number = {1},
 month = jan,
 year = {2017},
 issn = {1532-4435},
 pages = {826--830},
 numpages = {5},
 url = {http://dl.acm.org/citation.cfm?id=3122009.3122034},
 acmid = {3122034},
 publisher = {JMLR.org}
} 

@article{autoweka1,
  author    = {Thornton, Chris and Hoos, Holger H. and Hutter, Frank and Leyton-Brown, Kevin},
  title     = {Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of
               Classification Algorithms},
  journal   = {arXiv},
  year      = {2012},
  url       = {http://arxiv.org/abs/1208.3719},
}


@inproceedings{feurer2015efficient,
  title={Efficient and robust automated machine learning},
  author={Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2962--2970},
  year={2015}
}

@inproceedings{feurer-arxiv20a,
  title     = {Auto-Sklearn 2.0: The Next Generation},
  author    = {Matthias Feurer and Katharina Eggensperger and Stefan Falkner and Marius Lindauer and Frank Hutter},
  booktitle = {arXiv:2007.04074 [cs.LG]},
  year      = {2020}
}

@inproceedings{mcmahan2017communication,
  title={Communication-efficient learning of deep networks from decentralized data},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial intelligence and statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}

@article{li2018federated,
  title={Federated optimization in heterogeneous networks},
  author={Li, Tian and Sahu, Anit Kumar and Zaheer, Manzil and Sanjabi, Maziar and Talwalkar, Ameet and Smith, Virginia},
  journal={arXiv preprint arXiv:1812.06127},
  year={2018}
}
@inproceedings{chai2020tifl,
  title={Tifl: A tier-based federated learning system},
  author={Chai, Zheng and Ali, Ahsan and Zawad, Syed and Truex, Stacey and Anwar, Ali and Baracaldo, Nathalie and Zhou, Yi and Ludwig, Heiko and Yan, Feng and Cheng, Yue},
  booktitle={Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing},
  pages={125--136},
  year={2020}
}

@article{bonawitz2019towards,
  title={Towards federated learning at scale: System design},
  author={Bonawitz, Keith and Eichner, Hubert and Grieskamp, Wolfgang and Huba, Dzmitry and Ingerman, Alex and Ivanov, Vladimir and Kiddon, Chloe and Kone{\v{c}}n{\`y}, Jakub and Mazzocchi, Stefano and McMahan, H Brendan and others},
  journal={arXiv preprint arXiv:1902.01046},
  year={2019}
}

@article{abay2020mitigating,
  title={Mitigating bias in federated learning},
  author={Abay, Annie and Zhou, Yi and Baracaldo, Nathalie and Rajamoni, Shashank and Chuba, Ebube and Ludwig, Heiko},
  journal={arXiv preprint arXiv:2012.02447},
  year={2020}
}

@inproceedings{truex2019hybrid,
  title={A hybrid approach to privacy-preserving federated learning},
  author={Truex, Stacey and Baracaldo, Nathalie and Anwar, Ali and Steinke, Thomas and Ludwig, Heiko and Zhang, Rui and Zhou, Yi},
  booktitle={Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security},
  pages={1--11},
  year={2019}
}

@inproceedings{xu2019hybridalpha,
  title={Hybridalpha: An efficient approach for privacy-preserving federated learning},
  author={Xu, Runhua and Baracaldo, Nathalie and Zhou, Yi and Anwar, Ali and Ludwig, Heiko},
  booktitle={Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security},
  pages={13--23},
  year={2019}
}

@article{zhao2018federated,
  title={Federated learning with non-iid data},
  author={Zhao, Yue and Li, Meng and Lai, Liangzhen and Suda, Naveen and Civin, Damon and Chandra, Vikas},
  journal={arXiv preprint arXiv:1806.00582},
  year={2018}
}

@article{agarwal2018cpsgd,
  title={cpSGD: Communication-efficient and differentially-private distributed SGD},
  author={Agarwal, Naman and Suresh, Ananda Theertha and Yu, Felix and Kumar, Sanjiv and Mcmahan, H Brendan},
  journal={arXiv preprint arXiv:1805.10559},
  year={2018}
}

@article{konevcny2016federated,
  title={Federated learning: Strategies for improving communication efficiency},
  author={Kone{\v{c}}n{\`y}, Jakub and McMahan, H Brendan and Yu, Felix X and Richt{\'a}rik, Peter and Suresh, Ananda Theertha and Bacon, Dave},
  journal={arXiv preprint arXiv:1610.05492},
  year={2016}
}

@inproceedings{mohri2019agnostic,
  title={Agnostic federated learning},
  author={Mohri, Mehryar and Sivek, Gary and Suresh, Ananda Theertha},
  booktitle={International Conference on Machine Learning},
  pages={4615--4625},
  year={2019},
  organization={PMLR}
}


@article{karimireddy2019scaffold,
  title={SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.},
  author={Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank J and Stich, Sebastian U and Suresh, Ananda Theertha},
  year={2019}
}

@article{ludwig2020ibm,
  title={{IBM Federated Learning}: an enterprise framework white paper v0. 1},
  author={Ludwig, Heiko and Baracaldo, Nathalie and Thomas, Gegi and Zhou, Yi and Anwar, Ali and Rajamoni, Shashank and Ong, Yuya and Radhakrishnan, Jayaram and Verma, Ashish and Sinn, Mathieu and others},
  journal={arXiv preprint arXiv:2007.10987},
  year={2020},
  url={https://github.com/IBM/federated-learning-lib}
}

@article{kairouz2019advances,
  title={Advances and open problems in federated learning},
  author={Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and Bellet, Aur{\'e}lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Kallista and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others},
  journal={arXiv preprint arXiv:1912.04977},
  year={2019}
}

@inproceedings{li2021ditto,
  title={Ditto: Fair and robust federated learning through personalization},
  author={Li, Tian and Hu, Shengyuan and Beirami, Ahmad and Smith, Virginia},
  booktitle={International Conference on Machine Learning},
  pages={6357--6368},
  year={2021},
  organization={PMLR}
}

@article{smith2017federated,
  title={Federated multi-task learning},
  author={Smith, Virginia and Chiang, Chao-Kai and Sanjabi, Maziar and Talwalkar, Ameet},
  journal={arXiv preprint arXiv:1705.10467},
  year={2017}
}

@inproceedings{snoek2012practical,
  title={Practical bayesian optimization of machine learning algorithms},
  author={Snoek, J. and Larochelle, H. and Adams, R. P.},
  booktitle={Advances in neural information processing systems},
  year={2012},
}


@article{bergstra2012random,
  title={Random search for hyper-parameter optimization},
  author={Bergstra, James and Bengio, Yoshua},
  journal={Journal of Machine Learning Research},
  volume={13},
  number={Feb},
  pages={281--305},
  year={2012}
}


@inproceedings{sabharwal2016selecting,
  title={Selecting near-optimal learners via incremental data allocation},
  author={Sabharwal, Ashish and Samulowitz, Horst and Tesauro, Gerald},
  booktitle={Thirtieth AAAI Conference on Artificial Intelligence},
  year={2016}
}

@inproceedings{oh2019combinatorial,
  title={Combinatorial Bayesian optimization using the graph cartesian product},
  author={Oh, Changyong and Tomczak, Jakub M and Gavves, Efstratios and Welling, Max},
  booktitle={Proceedings of the 33rd International Conference on Neural Information Processing Systems},
  pages={2914--2924},
  year={2019}
}

@book{williams2006gaussian,
  title={Gaussian processes for machine learning},
  author={Williams, Christopher K and Rasmussen, Carl Edward},
  volume={2},
  number={3},
  year={2006},
  publisher={MIT press Cambridge, MA}
}


@article{breiman2001random,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@article{kantorovich1958space,
  title={On a space of totally additive functions},
  author={Kantorovich, Leonid Vasilevich and Rubinshtein, SG},
  journal={Vestnik of the St. Petersburg University: Mathematics},
  volume={13},
  number={7},
  pages={52--59},
  year={1958},
  publisher={Allerton Press, Inc.}
}

@article{kolouri2017optimal,
  title={Optimal mass transport: Signal processing and machine-learning applications},
  author={Kolouri, Soheil and Park, Se Rim and Thorpe, Matthew and Slepcev, Dejan and Rohde, Gustavo K},
  journal={IEEE signal processing magazine},
  volume={34},
  number={4},
  pages={43--59},
  year={2017},
  publisher={IEEE}
}

@article{wang2019adaptive,
      title={Adaptive Federated Learning in Resource Constrained Edge Computing Systems}, 
      author={Wang, Shiqiang  and Tuor, Tiffany  and  Salonidis, Theodoros and Leung, Kin and Makaya, Christian  and He, Ting and Chan, Kevin},
      year={2019},
      journal = {Journal Selected Areas in Communications (JSAC)}
}

@article{Xu20federated,
      title={Federated neural architecture search}, 
      author={Xu, Mengwei and Zhao, Yuxin and Bian, Kaigui and Huang, Gang and Mei, Qiaozhu and Liu, Xuanzhe},
      year={2020},
      journal = {arxiv.2002.06352}
}

@article{Garg20direct,
      title={Direct federated neural architecture search}, 
      author={Garg, Anubhav and Saha, Amit Kumar and Dutta, Debo},
      year={2020},
      journal = {arxiv.2010.06223}
}

@article{He20toward,
      title={Towards non-i.i.d. and invisible data with FedNAS: Federated deep learning via neural architecture search}, 
      author={He, Chaoyang and Annavaram, Murali and Avestimehr, Salman},
      year={2020},
      journal = {arxiv.2004.08546}
}

@InProceedings{pmlr-v54-mcmahan17a,
	title = 	 {Communication-Efficient Learning of Deep Networks from Decentralized Data},
	author = 	 {McMahan, B. and Moore, E. and Ramage, D. and Hampson, S. and Arcas, B. A.},
	booktitle = 	 {Proc. International Conference on Artificial Intelligence and Statistics},
	pages = 	 {1273--1282},
	year = 	 {2017},
	month = 	 {Apr},
	address = {Ft. Lauderdale, FL}
}


@article{villani2003topics,
  title={Topics in optimal transportation.(Books)},
  author={Villani, Cedric},
  journal={OR/MS Today},
  volume={30},
  number={3},
  pages={66--67},
  year={2003},
  publisher={Institute for Operations Research and the Management Sciences}
}

@inproceedings{kingma2015adam,
  author={Diederik P. Kingma and Jimmy Ba},
  title={Adam: A Method for Stochastic Optimization},
  year={2015},
  cdate={1420070400000},
  url={http://arxiv.org/abs/1412.6980},
  booktitle={ICLR (Poster)}
}

% Additions for AAAI2023

@article{Zoller2021, author = {Z\"{o}ller, Marc-Andr\'{e} and Huber, Marco F.}, title = {Benchmark and Survey of Automated Machine Learning Frameworks}, year = {2021}, issue_date = {May 2021}, publisher = {AI Access Foundation}, address = {El Segundo, CA, USA}, volume = {70}, issn = {1076-9757}, url = {https://doi.org/10.1613/jair.1.11854}, doi = {10.1613/jair.1.11854}, abstract = {Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.}, journal = {J. Artif. Int. Res.}, month = {may}, pages = {409–472}, numpages = {64} }

@inproceedings{Thornton2013, author = {Thornton, Chris and Hutter, Frank and Hoos, Holger H. and Leyton-Brown, Kevin}, title = {Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms}, year = {2013}, isbn = {9781450321747}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2487575.2487629}, doi = {10.1145/2487575.2487629}, abstract = {Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that attacks these issues separately. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA's standard distribution, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection and hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.}, booktitle = {Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages = {847–855}, numpages = {9}, keywords = {hyperparameter optimization, model selection, weka}, location = {Chicago, Illinois, USA}, series = {KDD '13} }

@article{Yang2019federated, author = {Yang, Qiang and Liu, Yang and Chen, Tianjian and Tong, Yongxin}, title = {Federated Machine Learning: Concept and Applications}, year = {2019}, issue_date = {March 2019}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {10}, number = {2}, issn = {2157-6904}, url = {https://doi.org/10.1145/3298981}, doi = {10.1145/3298981}, abstract = {Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.}, journal = {ACM Trans. Intell. Syst. Technol.}, month = {jan}, articleno = {12}, numpages = {19}, keywords = {transfer learning, GDPR, Federated learning} }

 @article{flora,
  doi = {10.48550/ARXIV.2202.08338},
  journal={arXiv preprint arXiv:2202.08338},
  url = {https://arxiv.org/abs/2202.08338},
  
  author = {Zhou, Yi and Ram, Parikshit and Salonidis, Theodoros and Baracaldo, Nathalie and Samulowitz, Horst and Ludwig, Heiko},
  
  keywords = {Machine Learning (cs.LG), Distributed, Parallel, and Cluster Computing (cs.DC), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm and Analysis},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}


 @article{flash_proof,
  doi = {},
  journal={},

  author = {Alam, Md Ibrahim Ibne and Kar, Koushik and Salonidis, Theodoros and Samulowitz, Horst},
  
  keywords = {},
  
  title = {{FLASH: Automating Federated Learning using CASH (Theoretical Proofs)}},
  
  publisher = {},
  
  year = {},
  
  copyright = {},
  note = {{https://tinyurl.com/FLASH-Theory-Proofs}}
}


@article{hsu2019measuring,
  title={Measuring the effects of non-identical data distribution for federated visual classification},
  author={Hsu, Tzu-Ming Harry and Qi, Hang and Brown, Matthew},
  journal={arXiv preprint arXiv:1909.06335},
  year={2019} 
}


@article{shwartz2022tabular,
  title={Tabular data: Deep learning is not all you need},
  author={Shwartz-Ziv, Ravid and Armon, Amitai},
  journal={Information Fusion},
  volume={81},
  pages={84--90},
  year={2022},
  publisher={Elsevier}
}



 @article{uai_2023_657_suppliment,
  doi = {},

  author = {Alam, Md Ibrahim Ibne and Kar, Koushik and Salonidis, Theodoros and Samulowitz, Horst},
  
  
  title = {{FLASH: Automating Federated Learning using CASH (Supplementary Material)}},
  
  year = {2023},
  
  copyright = {},
  note = {{https://tinyurl.com/UAI-2023-657}}
}


https://tinyurl.com/UAI-2023-657