Federated Learning for IoT: Ensuring Privacy and Security in Distributed Networks

Authors

  • Yashoda Krishna Kuppili Department Of CSE, GITAM SCHOOL OF TECHNOLOGY, GITAM UNIVERSITY, VISAKHAPATNAM
  • John Jaidhan B. Department Of CSE, GITAM SCHOOL OF TECHNOLOGY, GITAM UNIVERSITY, VISAKHAPATNAM

Keywords:

Federated Learning, IoT Security, Centralised Learning

Abstract

Federated learning is a machine learning technique in which the model is trained across a number of decentralized devices (clients) without the need to move the data to a central location. Because users’ data is stored on their devices rather than a central server or third party, this method gives consumers improved privacy and security. Contrarily, centralized learning mandates that users submit their data to a central server, raising issues with data security and privacy. The purpose of the current study was to evaluate and compare the effectiveness of the centralized and federated learning paradigms in the context of a simple regression task using simulated data. The findings demonstrated that while protecting user privacy, federated learning may attain accuracy levels that are on par with those of centralized learning. Our study also demonstrated the viability of implementing federated learning using well-known machine learning frameworks like TensorFlow Federated.

Downloads

Download data is not yet available.

References

Stacey Truex; Nathalie Baracaldo; Ali Anwar; Thomas Steinke; Heiko Ludwig; Rui Zhang; Yi Zhou; "A Hybrid Approach To PrivacyPreserving Federated Learning", ARXIV-CS.LG, 2018.

Mikhail Khodak; Maria-Florina Balcan; Ameet Talwalkar; "Adaptive Gradient-Based Meta-Learning Methods", ARXIV-CS.LG, 2019.

Latif U. Khan; Madyan Alsenwi; Ibrar Yaqoob; Muhammad Imran; Zhu Han; Choong Seon Hong; "Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries", IEEE ACCESS, 2020. .

Stefano Savazzi; Monica Nicoli; Vittorio Rampa; "Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks", IEEE INTERNET OF THINGS JOURNAL, 2020

Charles Wheelus; Xingquan Zhu; "IoT Network Security: Threats, Risks, and A Data-Driven Defense Framework", 2020.

Basheer Qolomany; Kashif Ahmad; Ala Al-Fuqaha; Junaid Qadir; "Particle Swarm Optimized Federated Learning For Industrial IoT And Smart City Services", ARXIV-CS.LG, 2020

Devrim Unal; Mohammad Hammoudeh; Muhammad Asif Khan; Abdelrahman Abuarqoub; Gregory Epiphaniou; Ridha Hamila; "Integration of Federated Machine Learning and Blockchain for The Provision of Secure Big Data Analytics for Internet of Things", COMPUTERS SECURITY, 2021.

Vijay Anavangot; Animesh Kumar; "Algorithms for Overpredictive Signal Analytics in Federated Learning", 2020 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2021.

] Amir Masoud Rahmani; Elham Azhir; Saqib Ali; Mokhtar Mohammadi; Omed Hassan Ahmed; Marwan Yassin Ghafour; Sarkar Hasan Ahmed; Mehdi Hosseinzadeh; "Artificial Intelligence Approaches and Mechanisms for Big Data Analytics: A Systematic Study", PEERJ. COMPUTER SCIENCE, 2021.

Othmane MARFOQ; Giovanni Neglia; Aurlien Bellet; Laetitia Kameni; Richard Vidal; "Federated Multi-Task Learning Under A Mixture of Distributions",

Ni, Wanli Liu, Yuanwei Yang, Zhaohui Tian, Hui Shen, Xuemin. (2021). Federated Learning in Multi-RIS Aided Systems. IEEE Internet of Things Journal. PP. 1-1. 10.1109/JIOT.2021.3130444.

Ghorpade, J. ., & Sonkamble, B. . (2023). Data-driven based Optimal Feature Selection Algorithm using Ensemble Techniques for Classification. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 33–41. https://doi.org/10.17762/ijritcc.v11i4.6378

Ólafur, S., Nieminen, J., Bakker, J., Mayer, M., & Schmid, P. Enhancing Engineering Project Management through Machine Learning Techniques. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/112

Agrawal, S. A., Umbarkar, A. M., Sherie, N. P., Dharme, A. M., & Dhabliya, D. (2021). Statistical study of mechanical properties for corn fiber with reinforced of polypropylene fiber matrix composite. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1072

Downloads

Published

02.09.2023

How to Cite

Kuppili, Y. K. ., & B., J. J. . (2023). Federated Learning for IoT: Ensuring Privacy and Security in Distributed Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 171–179. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3404

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.