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arXiv Machine Learning By Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato, Simone Palazzo

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

Read the original on arXiv Machine Learning →

FedCMAPSS is a new benchmark for federated learning applied to remaining useful life (RUL) estimation, built on the NASA C‑MAPSS dataset. It defines five standardized tasks that mimic real‑world industrial scenarios, from ideal IID conditions to highly heterogeneous data distributions. The paper evaluates state‑of‑the‑art federated optimization algorithms across multiple neural architectures, providing reproducible baselines and publicly available code and data splits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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