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FedCMAPSS Benchmark Advances Federated Learning for RUL Estimation

Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato, Simone Palazzo· August 28, 2026 View original

Key takeaways

  • FedCMAPSS is a new benchmark for federated RUL estimation.
  • It addresses data scarcity in predictive maintenance using federated learning.
  • The benchmark defines five standardized tasks simulating industrial challenges.
  • It provides reproducible baselines for comparing federated optimization algorithms.

Who benefits

ManufacturingAerospaceEnergyTransportationLogistics

Summary

This paper introduces FedCMAPSS, a new benchmark for federated learning in Remaining Useful Life (RUL) estimation, based on the NASA C-MAPSS dataset. It defines five standardized tasks simulating industrial challenges and provides reproducible baselines for developing and comparing federated predictive maintenance solutions.

In Industry 4.0, data-driven prognostics and health management (PHM) is crucial, but developing robust Remaining Useful Life (RUL) estimation models is often hindered by the scarcity of complete run-to-failure data. Federated learning (FL) offers a promising solution by enabling collaborative model training across multiple data silos without direct data sharing. However, the field has lacked a standardized evaluation framework.To address this gap, researchers have introduced FedCMAPSS, a new benchmark specifically designed for federated RUL estimation. Built upon the widely used NASA C-MAPSS dataset, FedCMAPSS defines five standardized tasks that realistically simulate various industrial challenges, ranging from ideal independent and identically distributed (IID) data settings to scenarios with extreme statistical heterogeneity.The paper presents a systematic evaluation of state-of-the-art federated optimization algorithms across multiple neural architectures, establishing reproducible baselines. By making the source code and data splits publicly available, FedCMAPSS aims to provide a common foundation for the development, comparison, and advancement of federated predictive maintenance solutions, fostering innovation in this critical area.

Why it matters

Professionals in industrial settings can now more effectively develop and benchmark federated learning solutions for predictive maintenance, leading to improved asset management, reduced downtime, and optimized operational costs.

How to implement this in your domain

  1. 1Explore the FedCMAPSS benchmark for evaluating federated learning models.
  2. 2Apply federated learning principles to RUL estimation in your industrial assets.
  3. 3Utilize the provided baselines to compare and improve predictive maintenance models.
  4. 4Collaborate with industry partners using federated learning without sharing raw data.

Original post by Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato, Simone Palazzo

"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…"

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Originally posted by Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato, Simone Palazzo on X · view source

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