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arXiv · 2608.26433

FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation

Amelia Sorrenti·Matteo Pennisi·Concetto Spampinato·Simone Palazzo
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Abstract

Data-driven prognostics and health management has emerged as a key enabler for Industry 4.0, yet the development of robust remaining useful life (RUL) estimation models is often limited by the scarcity of run-to-failure data. While federated learning offers a promising paradigm to collaboratively train predictive models without sharing sensor data, research efforts have operated so far in the absence of a common evaluation framework. To address this gap, this paper introduces FedCMAPSS, a benchmark for federated RUL estimation based on the commonly-used NASA C-MAPSS dataset. We define a set of five standardized tasks designed to simulate real-world industrial challenges, ranging from ideal IID settings to extreme statistical heterogeneity, and conduct a systematic evaluation of state-of-the-art federated optimization algorithms across multiple neural architectures. By establishing reproducible baselines and making the source code and data splits publicly available, this work aims to provide a standard foundation for developing and comparing federated predictive maintenance solutions.

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Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato, Simone Palazzo. 2026-08-26. FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation. https://arxiv.org/abs/2608.26433

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