# Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication

arXiv: https://arxiv.org/abs/2211.05662 · HTML: https://mlsanity.com/paper/2211.05662/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results](https://mlsanity.com/paper/1610.07930/) (2016)
- [Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning](https://mlsanity.com/paper/1708.09754/) (2017)
- [Federated Learning-based Active Authentication on Mobile Devices](https://mlsanity.com/paper/2104.07158/) (2021)
- [AuthCODE: A Privacy-preserving and Multi-device Continuous Authentication Architecture based on Machine and Deep Learning](https://mlsanity.com/paper/2004.07877/) (2020)
- [Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcare](https://mlsanity.com/paper/1805.11011/) (2018)

## Built on by (most cited first, 3 of 3)

- [CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication](https://mlsanity.com/paper/2405.07174/) (2024)
- [Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities](https://mlsanity.com/paper/2405.00394/) (2024)
- [Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection](https://mlsanity.com/paper/2410.17792/) (2024)

## Abstract (arXiv)

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.