mlsanityData to 14 Oct 2026

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

arXiv 2211.05662 ↗First posted 10 Nov 2022cs.LG cs.AI cs.CRcited by 3

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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.

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

14 of 42 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  6. 2018LEAF: A Benchmark for Federated Settingscited by 792
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  8. 2019Federated Machine Learning: Concept and Applicationscited by 412
  9. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  10. 2016Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Resultscited by 10
  11. 2017Implicit Smartphone User Authentication with Sensors and Contextual Machine Learningcited by 10
  12. 2021Federated Learning-based Active Authentication on Mobile Devicescited by 7
2 more
  1. 2020AuthCODE: A Privacy-preserving and Multi-device Continuous Authentication Architecture based on Machine and Deep Learningcited by 2
  2. 2018Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcarecited by 1

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authenticationcited by 2
  2. 2024Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Citiescited by 0
  3. 2024Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selectioncited by 0