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

arXiv机器学习 2026-08-28 12:00 1 阅读 查看原文

Computer Science > Machine Learning

arXiv:2608.26433 (cs)

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.
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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

Submission history

From: Amelia Sorrenti [view email]
[v1] Wed, 26 Aug 2026 22:18:03 UTC (548 KB)
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