Open-FARI: An Open-source testbed for Federated Anomaly detection in the Railway Industrial Internet of Things
Alessandra Rizzardi, Raffaele Della Corte, Jesús Fernando Cevallos Moreno, Simona De Vivo, Vittorio Orbinato, Sabrina Sicari, Domenico Cotroneo, Alberto Coen‐Porisini · 2025
The paper presents Open-FARI, an open-source testbed for evaluating federated learning algorithms for anomaly detection in the railway Industrial Internet of Things domain. Open-FARI uses synthetic data generation modules trained from real train sensor data to generate realistic sensor data of a fleet of trains. Generated data encompass normal and anomalous data, enabling the evaluation of federated learning algorithms for anomaly detection. The paper addresses the lack of testbeds and datasets tailored to the railway domain, which represents an obstacle to research on Machine Learning-driven solutions in this domain.