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Federated Learning for Artificial Intelligence in Embedded Systems
Review Article  ·  Published: 27 June 2025
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ICCK Transactions on Emerging Topics in Artificial Intelligence
Volume 2, Issue 2, 2025: 91-115
Review Article Open Access

Federated Learning for Artificial Intelligence in Embedded Systems

by
Kanthavel Radhakrishnan Kanthavel Radhakrishnan 1 ORCID
Dhaya Ramakrishnan Dhaya Ramakrishnan 1 * ORCID
R. Adline Freeda R. Adline Freeda 2 ORCID
1 School of ECE, PNG University of Technology, Papua New Guinea
2 KCG College of Technology, Chennai, India
* Corresponding Author: Dhaya Ramakrishnan, [email protected]
Volume 2, Issue 2
Volume 2, Issue 2 2025
Received: 21 March 2025 Accepted: 21 May 2025 Published: 27 June 2025 CrossMark
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DOI: 10.62762/TETAI.2025.440076
ARK: ark:/57805/tetai.2025.440076

Article Information

Published in ICCK Transactions on Emerging Topics in Artificial Intelligence
Volume/Issue Volume 2, Issue 2, 2025
Pages 91-115
Cited by 3 (Crossref) 4 (Scopus)

Abstract

Federated Learning (FL) which eliminates the centralized data storage requirement by facilitating model training on diverse edge devices is now a promising paradigm for decentralized machine learning (ML). Applications involving privacy-preserving Artificial Intelligence (AI), including wearable technology, IoT networks, and smart healthcare appliances, can particularly benefit from this solution in embedded systems. By using on-device local data from devices such as sensors, embedded controllers, and smartphones, FL keeps confidential information local, minimizing the data transfer cost and privacy risks. Potentiality, challenges, and key applications of FL integration with embedded systems are addressed in this paper. Device-to-device efficient communication, model updating, and trade-offs between model accuracy and computational resource limitations are some of the issues addressed. Also addressed in the paper are model aggregation, federated optimization methods, and their usage in edge-based AI in real-life applications. Problems with security, system reliability, and heterogeneous data in federated environments are also discussed in the paper. The extensive use of FL in embedded systems is one of the important developments in edge AI solution designing that is more scalable, secure, and privacy-conscious.

Graphical Abstract

Federated Learning for Artificial Intelligence in Embedded Systems

Keywords

federated learning embedded systems artificial intelligence edge computing privacy-preserving internet of things machine learning at the edge data privacy decentralized machine learning

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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Radhakrishnan, K., Ramakrishnan, D., & Freeda, R. A. (2025). Federated Learning for Artificial Intelligence in Embedded Systems. ICCK Transactions on Emerging Topics in Artificial Intelligence, 2(2), 91–115. https://doi.org/10.62762/TETAI.2025.440076
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TY  - JOUR
AU  - Radhakrishnan, Kanthavel
AU  - Ramakrishnan, Dhaya
AU  - Freeda, R. Adline
PY  - 2025
DA  - 2025/06/27
TI  - Federated Learning for Artificial Intelligence in Embedded Systems
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 2
IS  - 2
SP  - 91
EP  - 115
DO  - 10.62762/TETAI.2025.440076
UR  - https://www.icck.org/article/abs/TETAI.2025.440076
KW  - federated learning
KW  - embedded systems
KW  - artificial intelligence
KW  - edge computing
KW  - privacy-preserving internet of things
KW  - machine learning at the edge
KW  - data privacy
KW  - decentralized machine learning
AB  - Federated Learning (FL) which eliminates the centralized data storage requirement by facilitating model training on diverse edge devices is now a promising paradigm for decentralized machine learning (ML). Applications involving privacy-preserving Artificial Intelligence (AI), including wearable technology, IoT networks, and smart healthcare appliances, can particularly benefit from this solution in embedded systems. By using on-device local data from devices such as sensors, embedded controllers, and smartphones, FL keeps confidential information local, minimizing the data transfer cost and privacy risks. Potentiality, challenges, and key applications of FL integration with embedded systems are addressed in this paper. Device-to-device efficient communication, model updating, and trade-offs between model accuracy and computational resource limitations are some of the issues addressed. Also addressed in the paper are model aggregation, federated optimization methods, and their usage in edge-based AI in real-life applications. Problems with security, system reliability, and heterogeneous data in federated environments are also discussed in the paper. The extensive use of FL in embedded systems is one of the important developments in edge AI solution designing that is more scalable, secure, and privacy-conscious.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Radhakrishnan2025Federated,
  author = {Kanthavel Radhakrishnan and Dhaya Ramakrishnan and R. Adline Freeda},
  title = {Federated Learning for Artificial Intelligence in Embedded Systems},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2025},
  volume = {2},
  number = {2},
  pages = {91-115},
  doi = {10.62762/TETAI.2025.440076},
  url = {https://www.icck.org/article/abs/TETAI.2025.440076},
  abstract = {Federated Learning (FL) which eliminates the centralized data storage requirement by facilitating model training on diverse edge devices is now a promising paradigm for decentralized machine learning (ML). Applications involving privacy-preserving Artificial Intelligence (AI), including wearable technology, IoT networks, and smart healthcare appliances, can particularly benefit from this solution in embedded systems. By using on-device local data from devices such as sensors, embedded controllers, and smartphones, FL keeps confidential information local, minimizing the data transfer cost and privacy risks. Potentiality, challenges, and key applications of FL integration with embedded systems are addressed in this paper. Device-to-device efficient communication, model updating, and trade-offs between model accuracy and computational resource limitations are some of the issues addressed. Also addressed in the paper are model aggregation, federated optimization methods, and their usage in edge-based AI in real-life applications. Problems with security, system reliability, and heterogeneous data in federated environments are also discussed in the paper. The extensive use of FL in embedded systems is one of the important developments in edge AI solution designing that is more scalable, secure, and privacy-conscious.},
  keywords = {federated learning, embedded systems, artificial intelligence, edge computing, privacy-preserving internet of things, machine learning at the edge, data privacy, decentralized machine learning},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}

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