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Effcient Decentralized Deep Learning by Dynamic Model Averaging

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


Berlingerio, M.:
Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018; Proceedings, Part I
Cham: Springer International Publishing, 2019 (Lecture Notes in Artificial Intelligence 11051)
ISBN: 978-3-030-10924-0 (Print)
ISBN: 978-3-030-10925-7 (Online)
ISBN: 978-3-030-10926-4
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2018, Dublin>
Conference Paper
Fraunhofer IAIS ()

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.