---
res:
  bibo_abstract:
  - '<jats:title>Abstract</jats:title><jats:p>The 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<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>that
    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)<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>-B
    considers the whole ensemble as a single black box and optimizes the hyper-parameters
    of each individual model jointly, and (2)<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>-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<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>by
    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 (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>-B),
    run-time efficiency (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>-I),
    and statistical stability for both modes,<jats:inline-formula><jats:alternatives><jats:tex-math>$$\textit{MODES}$$</jats:tex-math><mml:math
    xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math></jats:alternatives></jats:inline-formula>outperforms
    the baseline, i.e., carry out tuning with MBO on each node individually with its
    local sub-data set.</jats:p>@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Junjie
      foaf_name: Shi, Junjie
      foaf_surname: Shi
  - foaf_Person:
      foaf_givenName: Jiang
      foaf_name: Bian, Jiang
      foaf_surname: Bian
  - foaf_Person:
      foaf_givenName: Jakob
      foaf_name: Richter, Jakob
      foaf_surname: Richter
  - foaf_Person:
      foaf_givenName: Kuan-Hsun
      foaf_name: Chen, Kuan-Hsun
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Jörg
      foaf_name: Rahnenführer, Jörg
      foaf_surname: Rahnenführer
  - foaf_Person:
      foaf_givenName: Haoyi
      foaf_name: Xiong, Haoyi
      foaf_surname: Xiong
  - foaf_Person:
      foaf_givenName: Jian-Jia
      foaf_name: Chen, Jian-Jia
      foaf_surname: Chen
  bibo_doi: 10.1007/s10994-021-06014-6
  bibo_issue: '6'
  bibo_volume: 110
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0885-6125
  - http://id.crossref.org/issn/1573-0565
  dct_language: eng
  dct_publisher: Springer Science and Business Media LLC@
  dct_title: 'MODES: model-based optimization on distributed embedded systems@'
...