CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication

TL;DR

This paper introduces CRSFL, a cluster-based resource-aware split federated learning framework that enhances continuous authentication by improving training efficiency, reducing overhead, and preserving privacy on resource-limited IoT devices.

Contribution

The study proposes a novel combination of clustering, split learning, and federated learning with genetic algorithm optimization to improve continuous authentication on heterogeneous devices.

Findings

Maintains high authentication accuracy.

Reduces training overhead and resource consumption.

Effectively handles device heterogeneity in IoT environments.

Abstract

In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) and Federated Learning (FL) have recently emerged as promising technologies for training a decentralized Machine Learning (ML) model. With the increasing use of smartphones and Internet of Things (IoT) devices, these distributed technologies enable users with limited resources to complete neural network model training with server assistance and collaboratively combine knowledge between different nodes. In this study, we propose combining these technologies to address the continuous authentication challenge while protecting user privacy and limiting device resource usage. However, the model's training is slowed due to SL sequential training and resource differences between IoT devices with different specifications. Therefore, we use a cluster-based approach to group devices with similar capabilities to mitigate the impact of slow devices while filtering out the devices incapable of training the model. In addition, we address the efficiency and robustness of training ML models by using SL and FL techniques to train the clients simultaneously while analyzing the overhead burden of the process. Following clustering, we select the best set of clients to participate in training through a Genetic Algorithm (GA) optimized on a carefully designed list of objectives. The performance of our proposed framework is compared to baseline methods, and the advantages are demonstrated using a real-life UMDAA-02-FD face detection dataset. The results show that CRSFL, our proposed approach, maintains high accuracy and reduces the overhead burden in continuous authentication scenarios while preserving user privacy.