Analysis of Statistical Data Heterogeneity in Federated Fault Identification



Published Sep 4, 2023
Zahra Taghiyarrenani Slawomir Nowaczyk Sepideh Pashami


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

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