Federated learning architecture for bearing fault diagnosis

Guan Ying Huang, Ching-Hung Lee

研究成果: Conference contribution同行評審

摘要

Federated learning (FL) is a distributed machine learning and it can obtain the participants, such as many companies or personal mobiles. The key point of federated learning is that it does not require the participants to send their data to the others, and FL only upload the gradients or weights of model trained by local data of each participant. Therefore, in this study, we combine the C fraction and gradient aggregation to implement the FL architecture for diagnosis of bearing fault. Finally, in our experiments, even if only a small number of clients participant in training, the testing accuracy can reach to 99 %. Furthermore, we use the number of turns to evaluate the impact of C fraction in testing.

原文English
主出版物標題Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面408-411
頁數4
ISBN(電子)9781665448482
DOIs
出版狀態Published - 26 8月 2021
事件2021 International Conference on System Science and Engineering, ICSSE 2021 - Virtual, Ho Chi Minh City, Viet Nam
持續時間: 26 8月 202128 8月 2021

出版系列

名字Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021

Conference

Conference2021 International Conference on System Science and Engineering, ICSSE 2021
國家/地區Viet Nam
城市Virtual, Ho Chi Minh City
期間26/08/2128/08/21

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