Centralised vs decentralised anomaly detection: when local and imbalanced data are beneficial

In this paper, we address the problem of anomaly detection in decentralised settings. We took inspiration from the current edge computing trend, pushing towards the development of decentralised ML algorithms, i.e., the devices that collected or generated data are in charge of collaborating to train the ML models without sharing raw data . The challenges connected to this scenario are (i) data distributions of local datasets might be different, (ii) data is very often unlabelled, and (iii) devices have limited computational resources. We address them by proposing an unsupervised ensemble method for decentralised anomaly detection where the base learners are lightweight autoencoders. We aim to investigate whether an ensemble of lightweight models trained in isolation on non-IID and unlabelled local data can compete with heavier models trained in centralised settings. In a task of multi-category anomaly detection, our results show that our method exploits the data imbalance successfully to make accurate predictions.