A CBR-Based Emergency Plan Generation Method Under the Federated Learning Framework
- 10.2991/978-94-6463-034-3_9How to use a DOI?
- Emergency decision-making; Federated learning; CBR; K-means
In order to use the emergency case data of multiple industrial enterprises to support the decision-making of target cases on the premise of protecting data privacy, this paper proposes a CBR-based emergency plan generation method under the framework of federated learning. First of all, each enterprise carries out feature extraction and case representation of local historical emergency cases. Then, based on the federated learning framework, under the premise of fully guaranteeing data privacy, each enterprise uses local data to collaboratively train a federated k-means model (FL-K-means) for the extraction of similar historical emergency cases. On this basis, we use the trained federal clustering model to extract similar cases across enterprises, reuse and adjust the information and experience of similar historical emergency cases, and then generate the emergency plan of the target case. On the premise of protecting enterprise data privacy, the method proposed in this paper can fully mine and utilize the emergency case data of different enterprises, effectively solve the data island problem between enterprises, support the decision-making of target emergency cases and generate the decision-making scheme of target emergency cases through the adjustment of similar historical emergency cases.
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Cite this article
TY - CONF AU - Qiang Gao AU - Ran Ran AU - Fei Hu AU - Zi-Mo Xing AU - Fu-Liang Zhang PY - 2022 DA - 2022/12/23 TI - A CBR-Based Emergency Plan Generation Method Under the Federated Learning Framework BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 66 EP - 73 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_9 DO - 10.2991/978-94-6463-034-3_9 ID - Gao2022 ER -