# Efficient Decentralized Deep Learning by Dynamic Model Averaging Source: [Efficient Decentralized Deep Learning by Dynamic Model Averaging]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:37.349Z Authors: Michael Kamp, Linara Adilova, Joachim Sicking, Fabian H\\"uger, Peter Schlicht, Tim Wirtz, Stefan Wrobel Published: 2018-07-09T15:01:51.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()