Anomalies and drift detection in decentralized learning environments

Abstract

Anomalies and drift detection in decentralized learning environments. The method includes deploying at a first node, (1) a local unsupervised autoencoder, trained at the first node, along with a local training data reference baseline for the first node, and (2) a global unsupervised autoencoder trained across a plurality of nodes, along with a corresponding global training data reference baseline. Production data at the first node is processed with local and global ML models deployed by a user. At least one of local and global anomaly data regarding anomalous production data or local and global drift data regarding drifting production data is derived based on the local and global training data reference baselines, respectively. At least one of the local anomaly data is compared with the global anomaly data or the local drift data with the global drift data for assessing impact of anomalies/drift on the ML models.