A federated learning setup with decentralized validation and four noise-robust aggregation rules improves remaining useful life predictions for five of six simulated airlines on the N-CMAPSS engine dataset compared with isolated training.
Accessed: 2025-05-20
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Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study
A federated learning setup with decentralized validation and four noise-robust aggregation rules improves remaining useful life predictions for five of six simulated airlines on the N-CMAPSS engine dataset compared with isolated training.