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Engineering Federated Learning Systems: A Literature Review

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Book cover Software Business (ICSOB 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 407))

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Abstract

With the increasing attention on Machine Learning applications, more and more companies are involved in implementing AI components into their software products in order to improve the service quality. With the rapid growth of distributed edge devices, Federated Learning has been introduced as a distributed learning technique, which enables model training in a large decentralized network without exchanging collected edge data. The method can not only preserve sensitive user data privacy but also save a large amount of data transmission bandwidth and the budget cost of computation equipment. In this paper, we provide a state-of-the-art overview of the empirical results reported in the existing literature regarding Federated Learning. According to the problems they expressed and solved, we then categorize those deployments into different application domains, identify their challenges and then propose six open research questions.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Göteborg, Sweden

    Hongyi Zhang & Jan Bosch

  2. Department of Computer Science and Media Technology, Malmö University, Östra Varvsgatan 11, 205 06, Malmö, Sweden

    Helena Holmström Olsson

Authors
  1. Hongyi Zhang
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  2. Jan Bosch
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  3. Helena Holmström Olsson
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Correspondence to Hongyi Zhang .

Editor information

Editors and Affiliations

  1. Blekinge Institute of Technology, Karlskrona, Sweden

    Eriks Klotins

  2. Blekinge Institute of Technology, Karlskrona, Sweden

    Krzysztof Wnuk

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Zhang, H., Bosch, J., Holmström Olsson, H. (2021). Engineering Federated Learning Systems: A Literature Review. In: Klotins, E., Wnuk, K. (eds) Software Business. ICSOB 2020. Lecture Notes in Business Information Processing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-67292-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-67292-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67291-1

  • Online ISBN: 978-3-030-67292-8

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