Analysis of Statistical Data Heterogeneity in Federated Fault Identification

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Published Sep 4, 2023
Zahra Taghiyarrenani Slawomir Nowaczyk Sepideh Pashami

Abstract

Federated Learning (FL) is a setting where different clients collaboratively train a Machine Learning model in a privacy-preserving manner, i.e., without the requirement to share data. Given the importance of security and privacy in real-world applications, FL is gaining popularity in many areas, including predictive maintenance. For example, it allows independent companies to construct a model collaboratively. However, since different companies operate in different environments, their working conditions may differ, resulting in heterogeneity among their data distributions. This paper considers the fault identification problem and simulates different scenarios of data heterogeneity. Such a setting remains challenging for popular FL algorithms, and thus we demonstrate the considerations to be taken into account when designing federated predictive maintenance solutions.  

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Keywords

Predictive Maintenance, Federated Learning, Predictive Maintenance Federated Learning Statistical Heterogeneity

References
Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Wortman, J. (2007). Learning bounds for domain adaptation. Advances in neural information processing systems, 20.

De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., . . . Tuytelaars, T. (2021). A continual learning survey: Defying forgetting in classification tasks. IEEE transactions on pattern analysis and machine intelligence, 44(7), 3366–3385.

Elly Treml, A., Andrade Flauzino, R., Suetake, M., & Ravazzoli Maciejewski, N. A. (2020). Experimental database for detecting and diagnosing rotor broken bar in a three-phase induction motor. IEEE Dataport.

Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021). Federated learning for internet of things: Recent advances, taxonomy, and open challenges. IEEE Communic. Surveys & Tutorials, 23(3), 1759–1799.

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273–1282).

Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. V. (2021). Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1622–1658.

Pandya, S., Srivastava, G., Jhaveri, R., Babu, M. R., Bhattacharya, S., Maddikunta, P. K. R., . . . Gadekallu, T. R. (2023). Federated learning for smart cities: A comprehensive survey. Sustainable Energy Technologies and Assessments, 55, 102987.

Taghiyarrenani, Z., & Berenji, A. (2022). An analysis of vibrations and currents for broken rotor bar detection in three-phase induction motors. In Phm society european conference (Vol. 7, pp. 43–48).

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Trans. on Int. Systems and Technology (TIST), 10(2), 1–19.

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.

Zhang, W., Li, X., Ma, H., Luo, Z., & Li, X. (2021). Federated learning for machinery fault diagnosis with dynamic validation and self-supervision. KnowledgeBased Systems, 213, 106679.

Zhao, Z., Mao, Y., Liu, Y., Song, L., Ouyang, Y., Chen, X., & Ding, W. (2023). Towards efficient communications in federated learning: A contemporary survey. Journal of the Franklin Institute.
Section
Regular Session Papers