Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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On-screen Activity Tracking Using Federated Learning

Authors
P. Padmini Rani1, K. Venkateswar Rao2, S. K. Salma2, M. Rupambika2, *, K. Poojitha2, L. Raghavendra2, N. Narendra Kumar2
1Assistant Professor, Department of CSE, Vignan’s Lara Institute of Technology &Science, Vadlamudi, Guntur, Andhra Pradesh, India
2UG Scholars, Department of CSE, Vignan’s Lara Institute of Technology &Science, Vadlamudi, Guntur, Andhra Pradesh, India
*Corresponding author. Email: mekapatirupambika@gmail.com
Corresponding Author
M. Rupambika
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_81How to use a DOI?
Keywords
decision tree; convolutional neural networks; linear discriminate analysis
Abstract

In this rapid technology of remote and online learning, the ability to monitor and assess students’ engagement and productivity has become increasingly vital. This paper presents a pioneering approach to addressing this challenge by combining privacy-preserving on-screen activity tracking with federated learning. Our revolutionary technology combines the benefits of real-time user monitoring with strong privacy protection, trying to discern whether students are productively using their time for knowledge development or unhappily wasting it. E-learning platforms have grown in popularity, especially in light of global events necessitating remote instruction; nonetheless, ensuring that students are actively engaged and focused throughout online sessions remains a key challenge. Our technique employs federated learning, a decentralized machine learning model, to guarantee user privacy while properly identifying on- screen actions.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_81How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Padmini Rani
AU  - K. Venkateswar Rao
AU  - S. K. Salma
AU  - M. Rupambika
AU  - K. Poojitha
AU  - L. Raghavendra
AU  - N. Narendra Kumar
PY  - 2024
DA  - 2024/07/30
TI  - On-screen Activity Tracking Using Federated Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 857
EP  - 865
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_81
DO  - 10.2991/978-94-6463-471-6_81
ID  - Rani2024
ER  -