aiqbench analysis
FedCMAPSS addresses the critical scarcity of run-to-failure data in industrial prognostics by introducing a standardized benchmark for federated learning on the NASA C-MAPSS dataset. By defining five tasks that simulate varying degrees of statistical heterogeneity, this work enables reproducible comparisons of optimization algorithms across different neural architectures without sharing sensitive sensor information. This establishes a necessary foundation for developing robust predictive maintenance solutions in Industry 4.0 environments.
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.
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.
Machine Learning (cs.LG)
arXiv:2608.26433 [cs.LG]
(or arXiv:2608.26433v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.26433 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 246 words
