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Today more data is gathered than ever before. This does not only hold for economic data, but also for research-data. At the same time, the need for ML-methods is higher than ever in research and other domains. Often the datasets used in ML-tasks are too big to be processed by a single work-station or even a single server. Distributed- and Federated Learning help to handle large datasets in a distributed way, even with privacy-guarantees in the case of Federated Learning.

This workshop will introduce methods of both, Distributed Learning and Federated Learning by giving both, a theoretical and practical view on these learning-frameworks.

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