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Leveraging the Potentials of Federated AI Ecosystems

In: Innovation Through Information Systems

Author

Listed:
  • Marco Röder

    (University of Würzburg)

  • Peter Kowalczyk

    (University of Würzburg)

  • Frédéric Thiesse

    (University of Würzburg)

Abstract

Deep learning increasingly receives attention due to its ability to efficiently solve various complex prediction tasks in organizations. It is therefore not surprising that more and more business processes are supported by deep learning. With the proliferation of edge intelligence, this trend will continue and, in parallel, new forms of internal and external cooperation are provided through federated learning. Hence, companies must deal with the potentials and pitfalls of these technologies and decide whether to deploy them or not and how. However, there currently is no domain-spanning decision framework to guide the efficient adoption of these technologies. To this end, the present paper sheds light on this research gap and proposes a research agenda to foster the potentials of value co-creation within federated AI ecosystems.

Suggested Citation

  • Marco Röder & Peter Kowalczyk & Frédéric Thiesse, 2021. "Leveraging the Potentials of Federated AI Ecosystems," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 61-68, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86800-0_5
    DOI: 10.1007/978-3-030-86800-0_5
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