---
_id: '66216'
abstract:
- lang: eng
text: 'AbstractThe predictive performance of a
machine learning model highly depends on the corresponding hyper-parameter setting.
Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires
the dedicated machine learning model to be trained and evaluated on centralized
data to obtain a performance estimate. However, in a distributed machine learning
scenario, it is not always possible to collect all the data from all nodes due
to privacy concerns or storage limitations. Moreover, if data has to be transferred
through low bandwidth connections it reduces the time available for tuning. Model-Based
Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters
but the application on distributed machine learning models or federated learning
lacks research. This work proposes a framework$$\textit{MODES}$$MODESthat
allows to deploy MBO on resource-constrained distributed embedded systems. Each
node trains an individual model based on its local data. The goal is to optimize
the combined prediction accuracy. The presented framework offers two optimization
modes: (1)$$\textit{MODES}$$MODES-B
considers the whole ensemble as a single black box and optimizes the hyper-parameters
of each individual model jointly, and (2)$$\textit{MODES}$$MODES-I
considers all models as clones of the same black box which allows it to efficiently
parallelize the optimization in a distributed setting. We evaluate$$\textit{MODES}$$MODESby
conducting experiments on the optimization for the hyper-parameters of a random
forest and a multi-layer perceptron. The experimental results demonstrate that,
with an improvement in terms of mean accuracy ($$\textit{MODES}$$MODES-B),
run-time efficiency ($$\textit{MODES}$$MODES-I),
and statistical stability for both modes,$$\textit{MODES}$$MODESoutperforms
the baseline, i.e., carry out tuning with MBO on each node individually with its
local sub-data set.'
author:
- first_name: Junjie
full_name: Shi, Junjie
last_name: Shi
- first_name: Jiang
full_name: Bian, Jiang
last_name: Bian
- first_name: Jakob
full_name: Richter, Jakob
last_name: Richter
- first_name: Kuan-Hsun
full_name: Chen, Kuan-Hsun
last_name: Chen
- first_name: Jörg
full_name: Rahnenführer, Jörg
last_name: Rahnenführer
- first_name: Haoyi
full_name: Xiong, Haoyi
last_name: Xiong
- first_name: Jian-Jia
full_name: Chen, Jian-Jia
last_name: Chen
citation:
ama: 'Shi J, Bian J, Richter J, et al. MODES: model-based optimization on distributed
embedded systems. Machine Learning. 2021;110(6):1527-1547. doi:10.1007/s10994-021-06014-6'
apa: 'Shi, J., Bian, J., Richter, J., Chen, K.-H., Rahnenführer, J., Xiong, H.,
& Chen, J.-J. (2021). MODES: model-based optimization on distributed embedded
systems. Machine Learning, 110(6), 1527–1547. https://doi.org/10.1007/s10994-021-06014-6'
bibtex: '@article{Shi_Bian_Richter_Chen_Rahnenführer_Xiong_Chen_2021, title={MODES:
model-based optimization on distributed embedded systems}, volume={110}, DOI={10.1007/s10994-021-06014-6},
number={6}, journal={Machine Learning}, publisher={Springer Science and Business
Media LLC}, author={Shi, Junjie and Bian, Jiang and Richter, Jakob and Chen, Kuan-Hsun
and Rahnenführer, Jörg and Xiong, Haoyi and Chen, Jian-Jia}, year={2021}, pages={1527–1547}
}'
chicago: 'Shi, Junjie, Jiang Bian, Jakob Richter, Kuan-Hsun Chen, Jörg Rahnenführer,
Haoyi Xiong, and Jian-Jia Chen. “MODES: Model-Based Optimization on Distributed
Embedded Systems.” Machine Learning 110, no. 6 (2021): 1527–47. https://doi.org/10.1007/s10994-021-06014-6.'
ieee: 'J. Shi et al., “MODES: model-based optimization on distributed embedded
systems,” Machine Learning, vol. 110, no. 6, pp. 1527–1547, 2021, doi:
10.1007/s10994-021-06014-6.'
mla: 'Shi, Junjie, et al. “MODES: Model-Based Optimization on Distributed Embedded
Systems.” Machine Learning, vol. 110, no. 6, Springer Science and Business
Media LLC, 2021, pp. 1527–47, doi:10.1007/s10994-021-06014-6.'
short: J. Shi, J. Bian, J. Richter, K.-H. Chen, J. Rahnenführer, H. Xiong, J.-J.
Chen, Machine Learning 110 (2021) 1527–1547.
date_created: 2026-07-03T21:20:17Z
date_updated: 2026-07-05T14:46:07Z
doi: 10.1007/s10994-021-06014-6
intvolume: ' 110'
issue: '6'
language:
- iso: eng
page: 1527-1547
publication: Machine Learning
publication_identifier:
issn:
- 0885-6125
- 1573-0565
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: 'MODES: model-based optimization on distributed embedded systems'
type: journal_article
user_id: '128464'
volume: 110
year: '2021'
...