AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (4.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Boosting for Distributed Online Convex Optimization

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Medical Engineering Laboratory of Chinese PLA General Hospital
School of Cyberspace Security, Dongguan University of Technology, Dongguan 523000, China
Show Author Information

Abstract

Decentralized Online Learning (DOL) extends online learning to the domain of distributed networks. However, limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized methods. Considering the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network, applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models. A new boosting method, namely Boosting for Distributed Online Convex Optimization (BD-OCO), is designed to realize the application of boosting in distributed scenarios. BD-OCO achieves the regret upper bound 𝒪⁢(M+NM⁢N⁢T), where M measures the size of the distributed network and N is the number of Weak Learners (WLs) in each node. The core idea of BD-OCO is to apply the local model to train a strong global one. BD-OCO is evaluated on the basis of eight different real-world datasets. Numerical results show that BD-OCO achieves excellent performance in accuracy and convergence, and is robust to the size of the distributed network.

Keywords

distributed Online Convex Optimization (OCO)online boostingOnline Gradient Boosting (OGB)

References

[1]
W. Chen, Y. J. Wang, and Y. Yuan, Combinatorial multi-armed bandit: General framework and applications, in Proc. of the 30th Int. Conf. on Machine Learning (ICML), Atlanta, GA, USA, 2013, pp. 151–159.
Google Scholar
[2]
B. L. Pereira, A. Ueda, G. Penha, R. L. T. Santos, and N. Ziviani, Online learning to rank for sequential music recommendation, in Proc. of the 13th ACM Conf. on Recommender Systems, Copenhagen, Dennark, 2019, pp. 237–245.
Crossref Google Scholar
[3]
Y. W. Zhao, C. Yu, P. L. Zhao, H. L. Tang, S. Qiu, and J. Liu, Decentralized online learning: Take benefits from others’ data without sharing your own to track global trend, arXiv preprint arXiv: 1901.10593, 2019.
Google Scholar
[4]
S. Shahrampour and A. Jadbabaie, Distributed online optimization in dynamic environments using mirror descent, IEEE Trans. Automat. Control, vol. 63, no. 3, pp. 714–725, 2018.
Crossref Google Scholar
[5]
A. Koppel, S. Paternain, C. Richard, and A. Ribeiro, Decentralized online learning with kernels, IEEE Trans. Signal Process., vol. 66, no. 12, pp. 3240–3255, 2018.
Crossref Google Scholar
[6]
N. Bastianello and E. Dall’Anese, Distributed and inexact proximal gradient method for online convex optimization, in Proc. 2021 European Control Conf. (ECC), Delft, the Netherlands, 2021, pp. 2432–2437.
Crossref Google Scholar
[7]
C. Zhang, P. L. Zhao, S. J. Hao, Y. C. Soh, B. S. Lee, C. Y. Miao, and S. C. H. Hoi, Distributed multi-task classification: A decentralized online learning approach, Mach. Learn., vol. 107, no. 4, pp. 727–747, 2018.
Crossref Google Scholar
[8]
T. G. Dietterich, Ensemble methods in machine learning, in Proc. Int. Workshop on Multiple Classifier Systems, Cagliari, Italy, 2000, pp. 1–15.
Crossref Google Scholar
[9]
J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour, Boosting methods for multi-class imbalanced data classification: An experimental review, J. Big Data, vol. 7, p. 70, 2020.
Crossref Google Scholar
[10]
N. C. Oza and S. J. Russell, Online bagging and boosting, in Proc. of the Eighth Int. Workshop on Artificial Intelligence and Statistics, Key West, FL, USA, 2001, pp. 229–236.
Google Scholar
[11]
S. T. Chen, H. T. Lin, and C. J. Lu, An online boosting algorithm with theoretical justifications, in Proc. of the 29th Int. Conf. on Int. Conf. on Machine Learning, Edinburgh, UK, 2012, pp. 1873–1880.
Google Scholar
[12]
A. Beygelzimer, E. Hazan, S. Kale, and H. P. Luo, Online gradient boosting, in Proc. of the 28th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, 2015, pp. 2458–2466.
Google Scholar
[13]
E. Hazan and K. Singh, Boosting for online convex optimization, in Proc. of the 38th Int. Conf. on Machine Learning, Vienna, Austria, 2021, pp. 4140–4149.
Google Scholar
[14]
Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, in Proc. of the Thirteenth Int. Conf. on Int. Conf. on Machine Learning, Bari, Italy, 1996, pp. 148–156.
Google Scholar
[15]
T. Zhang and B. Yu, Boosting with early stopping: Convergence and consistency, Ann. Statist., vol. 33, no. 4, pp. 1538–1579, 2005.
Crossref Google Scholar
[16]
M. Raginsky, N. Kiarashi, and R. Willett, Decentralized online convex programming with local information, in Proc. of the 2011 American Control Conf., San Francisco, CA, USA, 2011, pp. 5363–5369.
Crossref Google Scholar
[17]
A. Nedić, S. Lee, and M. Raginsky, Decentralized online optimization with global objectives and local communication, in Proc. 2015 American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 4497–4503.
Crossref Google Scholar
[18]
J. Y. Jiang, W. P. Zhang, J. J. Gu, and W. W. Zhu, Asynchronous decentralized online learning, in Proc. of the 35th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, 2021, pp. 20185–20196.
Google Scholar
[19]
E. Hazan, Introduction to online convex optimization, Foundations and Trends in Optimization, vol. 2, nos. 3&4, pp. 157–325, 2016.
Crossref Google Scholar
[20]
S. Shalev-Shwartz, Online learning and online convex optimization, Foundat. Trends Mach. Learn., vol. 4, no. 2, pp. 107–194, 2012.
Crossref Google Scholar
[21]
Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119–139, 1997.
Crossref Google Scholar
[22]
J. H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Statist., vol. 29, no. 5, pp. 1189–1232, 2001.
Crossref Google Scholar
[23]
T. Parag, F. Porikli, and A. Elgammal, Boosting adaptive linear weak classifiers for online learning and tracking, in Proc. 2008 IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 1–8.
Crossref Google Scholar
[24]
A. Beygelzimer, S. Kale, and H. P. Luo, Optimal and adaptive algorithms for online boosting, in Proc. of the 32nd Int. Conf. on Int. Conf. on Machine Learning, Lille, France, 2015, pp. 2323–2331.
Google Scholar
[25]
Y. H. Jung, J. Goetz, and A. Tewari, Online multiclass boosting, in Proc. of the 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, USA, 2017. 920–929.
Google Scholar
[26]
X. B. An, C. Hu, G. Liu, and H. S. Lin, Distributed online gradient boosting on data stream over multi-agent networks, Signal Process., vol. 189, p. 108253, 2021.
Crossref Google Scholar
[27]
J. C. Duchi, A. Agarwal, and M. J. Wainwright, Dual averaging for distributed optimization: Convergence analysis and network scaling, IEEE Trans. Automat. Control, vol. 57, no. 3, pp. 592–606, 2012.
Crossref Google Scholar
[28]
D. Garber and E. Hazan, A linearly convergent conditional gradient algorithm with applications to online and stochastic optimization, arXiv preprint arXiv: 1301.4666, 2013.
Google Scholar
Tsinghua Science and Technology
Volume 28 Issue 4,
August 2023
Pages 811-821
DOI: 10.26599/TST.2022.9010041
Cite this article:
Hu Y, Zhao Y, Luo L, et al. Boosting for Distributed Online Convex Optimization. Tsinghua Science and Technology, 2023, 28(4): 811-821. https://doi.org/10.26599/TST.2022.9010041

1110

Views

201

Downloads

1

Crossref

1

Web of Science

1

Scopus

0

CSCD

Altmetrics
Received: 06 September 2022
Revised: 18 September 2022
Accepted: 20 September 2022
Published: 06 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return