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  • Weight Exchange in Distributed Learning 

    Authors:Dorner, Julian; Favrichon, Samuel; Öǧrenci, Arif Selçuk
    Publisher and Date:(IEEE, 2016)
    Neural networks may allow different organisations to extract knowledge from the data they collect about a similar problem domain. Moreover learning algorithms usually benefit from being able to use more training instances. But the parties owning the data are not always keen on sharing it. We propose a way to implement distributed learning to improve the performance of neural networks without sharing the actual data among different organisations. This paper deals with the alternative methods of ...