FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
arXiv:2608.26433v1 Announce Type: new 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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[Submitted on 26 Aug 2026]
Title:FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
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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.
Comments: Accepted at the 21st IEEE Conference on Industrial Electronics and Applications (ICIEA 2026)
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2608.26433 [cs.LG]
(or arXiv:2608.26433v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.26433
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Amelia Sorrenti [view email] [v1] Wed, 26 Aug 2026 22:18:03 UTC (548 KB)
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