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Feature distribution drift is not the same as label drift and neither is the same as business metric degradation. Confusing them leads to alert fatigue.
Federated learning is compelling for privacy-preserving training on edge devices. The system-level challenges — heterogeneous hardware, intermittent connectivity, staleness — make it harder than the papers suggest.
An LSTM autoencoder trained on normal sensor readings detects anomalies by reconstruction error. The threshold selection, false-positive rate, and retraining cadence matter more than the architecture.
Missing hyperparameters, unreported data cleaning steps, and hardware-dependent results make ML reproducibility harder than most papers admit.