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Decision making and operational management of multiple identical units, equipment, or plants (sources) that are geographically distributed are becoming increasingly common. Instead of developing a classifier for each source, with much less data and representativeness of the entire population, this work presents a unique centralized classifier using a federated approach. The centralized classifier can take into account all data collected from multiple distributed sources and accommodate their local and specific data structures and correlation patterns. The federated approach has the built-in capability of expansion, allowing for the inclusion of more sources, without the need for retraining. Therefore, new sources may immediately benefit from the existing unified classification model as soon as they are “connected,” which only requires a projection operation. The proposed federated approach is applied to a real case study of predicting an important property (coagulation) of waste lubricant oil (WLO) in several locations of the recovery network. The coagulation behavior determines if WLO can be regenerated and used to produce again base oil. Fourier transform infrared spectra are collected at different sources (laboratories) and combined using the federated framework, leading to high classification accuracy and a more generalizable model.

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