Abstract
In industrial applications of AI, challenges for visual inspection include data shortage and security. In this paper, we propose a Federated Learning (FL) framework to address these issues. This method is incorporated with our novel DataonomySM approach which can overcome the limited size of industrial dataset in each inspection task. The models pre-trained in the server can continuously and regularly update, and help each client upgrade its inspection model over time. The FL approach only requires clients to send to the server certain information derived from raw images, and thus does not sacrifice data security. Some preliminary tests are done to examine the workability of the proposed framework. This study is expected to bring the field of automated inspection to a new level of security, reliability, and efficiency, and to unlock significant potentials of deep learning applications.
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Han, X., Yu, H., Gu, H. (2019). Visual Inspection with Federated Learning. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_5
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DOI: https://doi.org/10.1007/978-3-030-27272-2_5
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