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

• URL: http://arxiv.org/abs/2608.26433v1
• Date: Wed, 26 Aug 2026 22:18:03 GMT
• Title: FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
• Abstract summary: FedCMAPSS is a benchmark for federated RUL estimation based on the NASA C-MAPSS dataset.<n>This work aims to provide a standard foundation for developing and comparing federated predictive maintenance solutions.
• Score: 14.659921465860565
• License: http://creativecommons.org/licenses/by/4.0/
• 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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