A Federated Learning Architecture for Anomaly Detection on the Edge Using Deep Autoencoders

Autoencoder networks are widely used in anomaly detection, however, their training can be computationally expensive, limiting their use in Edge Computing and Federated Learning scenarios, where devices are usually not very powerful. In addition, there are several ways to directly or indirectly attack the privacy of the data used by these networks, which is unacceptable in this type of scenario. Unlike traditional autoencoder networks, Deep AutoEncoder for Federated learning (DAEF) does not require several rounds of learning since it is a non-iterative method. This implies greater speed, less network traffic, and lower energy consumption, as well as preventing the privacy attacks common in iterative networks. In this paper, we present an architecture designed for the use of the DAEF network in Edge Computing and Federated Learning scenarios. Unlike other collaborative machine learning approaches, it is not server based. Consequently, all the stages of the learning process, including the model aggregation, are performed on the edge devices (nodes). Each edge node trains its local DAEF network asynchronously concerning the rest. Nodes that have completed their training can voluntarily request to add their local model information to the global one. In this way, there is not a unique aggregator node, but each node in the network will be in charge of adding its local learning to the global model. The federated learning management is handled by a coordinator node using a Message Queuing Telemetry Transport (MQTT) communication protocol, which can be assumed by any device in the network in case of failures, and the information exchanged between the nodes does not compromise the privacy of the original local datasets.