Efficient decentralized deep learning by dynamic model averaging

Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

24 Citations (Scopus)


We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones. Code related to this paper is available at: https://bitbucket.org/Michael_Kamp/decentralized-machine-learning.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2018 Dublin, Ireland, September 10–14, 2018 Proceedings, Part I
EditorsMichele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim
Place of PublicationCham Switzerland
Number of pages17
ISBN (Electronic)9783030109257
ISBN (Print)9783030109240
Publication statusPublished - 2019
Externally publishedYes
EventEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases 2018 - Dublin, Ireland
Duration: 10 Sep 201814 Sep 2018
https://link.springer.com/book/10.1007/978-3-030-10925-7 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases 2018
Abbreviated titleECML PKDD 2018
Internet address

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