Computational Efficiency Improvement in Time Series Anomaly Detection with Federated Learning for Reservoir Computing Models

Abstract

Federated Learning is a decentralized machine learning method without aggregating raw data, making it a promising approach to preserve data privacy. While a lot of methods for building deep learning models with federated learning have been proposed, IoT applications face some limitations such as communication bandwidth between servers and clients and restricted computational resources of client devices. To address these challenges, this study focuses on federated learning based on reservoir computing models to improve efficiency in terms of computation and communication. A previous method for time-series anomaly detection assumes that clients simultaneously transmit local updates to the server and that the server updates the global model once. This study aims to enhance the efficiency of the previous method by proposing an approach that uses principal component analysis (PCA) to efficiently update the global model online. We clarify the tradeoff between computational performance and efficiency of the proposed approach through numerical experiments, towards achieving both efficient global model updates and reduced data communication cost.