A Federated Learning Approach for Anomaly Detection in High Performance Computing

High Performance Computing (HPC) systems are complex machines that need to be operated at their maximum potential to recoup their investment cost and to mitigate their environmental impact. Anomalous conditions hindering the correct usage of the supercomputing nodes are a significant problem. Hence, the development of automated anomaly detection techniques remains a vital area of research. Machine Learning (ML) models demonstrated to be good at detecting anomalies on individual nodes. However, the potential of combining data from multiple computing nodes and associated ML models has not been explored yet. Federated Learning (FL) can address this shortcoming, by allowing individual models to learn from each other. This paper applies FL to improve the performance of anomaly detection models for HPC systems. The approach has been validated on data from an actual supercomputer, obtaining an improvement in the average f-score from 0.31 to 0.84. We also show how FL can significantly shorten the data collection period needed to create a training set. While ML models need, on average, 4.5 months of training data, FL reduces the training set size to 1.2 weeks - a 15x reduction.