Unsupervised anomalies detection in IIoT edge devices networks using federated learning

TL;DR

This paper explores federated learning for anomaly detection in IIoT edge devices, demonstrating comparable performance to centralized models while addressing privacy concerns, and discusses issues like fairness and false alarms.

Contribution

It implements Fedavg on modern IIoT datasets, evaluates its limitations, and proposes a new Fair Fedavg algorithm to improve training fairness and accuracy.

Findings

Fedavg achieves similar accuracy to centralized models.

Unfairness in Fedavg can cause false alarms in intrusion detection.

Proposed Fair Fedavg aims to enhance training fairness.

Abstract

In a connection of many IoT devices that each collect data, normally training a machine learning model would involve transmitting the data to a central server which requires strict privacy rules. However, some owners are reluctant of availing their data out of the company due to data security concerns. Federated learning(FL) as a distributed machine learning approach performs training of a machine learning model on the device that gathered the data itself. In this scenario, data is not share over the network for training purpose. Fedavg as one of FL algorithms permits a model to be copied to participating devices during a training session. The devices could be chosen at random, and a device can be aborted. The resulting models are sent to the coordinating server and then average models from the devices that finished training. The process is repeated until a desired model accuracy is achieved. By doing this, FL approach solves the privacy problem for IoT/ IIoT devices that held sensitive data for the owners. In this paper, we leverage the benefits of FL and implemented Fedavg algorithm on a recent dataset that represent the modern IoT/ IIoT device networks. The results were almost the same as the centralized machine learning approach. We also evaluated some shortcomings of Fedavg such as unfairness that happens during the training when struggling devices do not participate for every stage of training. This inefficient training of local or global model could lead in a high number of false alarms in intrusion detection systems for IoT/IIoT gadgets developed using Fedavg. Hence, after evaluating the FedAv deep auto encoder with centralized deep auto encoder ML, we further proposed and designed a Fair Fedavg algorithm that will be evaluated in the future work.