  Continuous behavioural authentication methods add a unique layer of security
by allowing individuals to verify their unique identity when accessing a
device. Maintaining session authenticity is now feasible by monitoring users'
behaviour while interacting with a mobile or Internet of Things (IoT) device,
making credential theft and session hijacking ineffective. Such a technique is
made possible by integrating the power of artificial intelligence and Machine
Learning (ML). Most of the literature focuses on training machine learning for
the user by transmitting their data to an external server, subject to private
user data exposure to threats. In this paper, we propose a novel Federated
Learning (FL) approach that protects the anonymity of user data and maintains
the security of his data. We present a warmup approach that provides a
significant accuracy increase. In addition, we leverage the transfer learning
technique based on feature extraction to boost the models' performance. Our
extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and
UMDAA-02-FD, show a significant increase in user authentication accuracy while
maintaining user privacy and data security.
