Abstract
Federated Learning, as a distributed learning technique, has emerged with the improvement of the performance of IoT and edge devices. The emergence of this learning method alters the situation in which data must be centrally uploaded to the cloud for processing and maximizes the utilization of edge devices’ computing and storage capabilities. The learning approach eliminates the need to upload large amounts of local data and reduces data transfer latency with local data processing. Since the Federated Learning technique does not require centralized data for model training, it is better suited to edge learning scenarios in which nodes have limited data. However, despite the fact that Federated Learning has significant benefits, we discovered that companies struggle with integrating Federated Learning components into their systems. In this paper, we present case study research that describes reasons why companies anticipate Federated Learning as an applicable technique. Secondly, we summarize the services that a complete Federated Learning system needs to support in industrial scenarios and then identify the key challenges for industries to adopt and transition to Federated Learning. Finally, based on our empirical findings, we suggest five criteria for companies implementing reliable Federated Learning systems.
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Acknowledgements
This work was partially supported by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, the Software Center and the Chalmers AI Research Center. The authors would also like to express their gratitude for all the interviewees and the support provided by Ericsson.
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Zhang, H., Dakkak, A., Mattos, D.I., Bosch, J., Olsson, H.H. (2021). Towards Federated Learning: A Case Study in the Telecommunication Domain. In: Wang, X., Martini, A., Nguyen-Duc, A., Stray, V. (eds) Software Business. ICSOB 2021. Lecture Notes in Business Information Processing, vol 434. Springer, Cham. https://doi.org/10.1007/978-3-030-91983-2_18
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