Federated LSTM autoencoders for time series anomaly detection in production-scale HPC systems

High-Performance Computing (HPC) systems are becoming increasingly vulnerable to anomalies as their scale and complexity grow. In this work, we propose a federated learning (FL) framework that integrates Long Short-Term Memory (LSTM) autoencoders for time series anomaly detection, allowing decentralized model training without sharing raw data. Using real telemetry from the Marconi100 Tier-0 supercomputer, our approach improves the average F1-score from 0.388 to 0.867 (+123 %) and the AUC from 0.334 to 0.808 (+142 %). It also cuts the training data requirement by a factor of 15, reducing the collection period from 4.5 months to just 1.25 weeks. These improvements are consistent across unsupervised, semi-supervised, and supervised settings, and significance testing with the Wilcoxon signed-rank test confirms they are statistically robust (p < 0.01). To our knowledge, this is the first comprehensive evaluation of FL-based LSTM autoencoders for anomaly detection in real HPC environments.