  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.
