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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 Electrical & Communications Engineering, PNG University of Technology, Lae, 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 4 (Crossref) 4 (Scopus)

Abstract

Federated Learning (FL) has emerged as a principled paradigm for privacy-preserving decentralized machine learning, enabling model training across distributed embedded devices without centralizing sensitive data. This review examines FL as applied to resource-constrained embedded and edge AI systems, encompassing its architectural foundations, principal optimization algorithms, application domains, and security mechanisms. We analyze the interplay between FL's theoretical properties and the practical constraints imposed by heterogeneous embedded hardware, non-IID data distributions, bandwidth-limited IoT networks, and adversarial threat models. Application domains examined in depth include smart healthcare, autonomous vehicles, industrial IoT, precision agriculture, smart home automation, and urban infrastructure, with attention to how FL enables privacy-preserving AI deployment in each context. We further review the principal techniques that make embedded FL viable---including FedAvg, FedProx, model compression, gradient sparsification, differential privacy, secure multi-party computation, and blockchain-secured aggregation---and identify open research gaps in heterogeneous model aggregation and adaptive privacy protection. Future directions encompassing TinyML integration, federated reinforcement learning, and next-generation 5G/6G network infrastructure are discussed. The review aims to provide a technically grounded reference for researchers and practitioners seeking to deploy FL in real-world embedded environments where privacy, energy efficiency, and communication constraints are simultaneously binding.

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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APA Style
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) has emerged as a principled paradigm for privacy-preserving decentralized machine learning, enabling model training across distributed embedded devices without centralizing sensitive data. This review examines FL as applied to resource-constrained embedded and edge AI systems, encompassing its architectural foundations, principal optimization algorithms, application domains, and security mechanisms. We analyze the interplay between FL's theoretical properties and the practical constraints imposed by heterogeneous embedded hardware, non-IID data distributions, bandwidth-limited IoT networks, and adversarial threat models. Application domains examined in depth include smart healthcare, autonomous vehicles, industrial IoT, precision agriculture, smart home automation, and urban infrastructure, with attention to how FL enables privacy-preserving AI deployment in each context. We further review the principal techniques that make embedded FL viable---including FedAvg, FedProx, model compression, gradient sparsification, differential privacy, secure multi-party computation, and blockchain-secured aggregation---and identify open research gaps in heterogeneous model aggregation and adaptive privacy protection. Future directions encompassing TinyML integration, federated reinforcement learning, and next-generation 5G/6G network infrastructure are discussed. The review aims to provide a technically grounded reference for researchers and practitioners seeking to deploy FL in real-world embedded environments where privacy, energy efficiency, and communication constraints are simultaneously binding.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@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) has emerged as a principled paradigm for privacy-preserving decentralized machine learning, enabling model training across distributed embedded devices without centralizing sensitive data. This review examines FL as applied to resource-constrained embedded and edge AI systems, encompassing its architectural foundations, principal optimization algorithms, application domains, and security mechanisms. We analyze the interplay between FL's theoretical properties and the practical constraints imposed by heterogeneous embedded hardware, non-IID data distributions, bandwidth-limited IoT networks, and adversarial threat models. Application domains examined in depth include smart healthcare, autonomous vehicles, industrial IoT, precision agriculture, smart home automation, and urban infrastructure, with attention to how FL enables privacy-preserving AI deployment in each context. We further review the principal techniques that make embedded FL viable---including FedAvg, FedProx, model compression, gradient sparsification, differential privacy, secure multi-party computation, and blockchain-secured aggregation---and identify open research gaps in heterogeneous model aggregation and adaptive privacy protection. Future directions encompassing TinyML integration, federated reinforcement learning, and next-generation 5G/6G network infrastructure are discussed. The review aims to provide a technically grounded reference for researchers and practitioners seeking to deploy FL in real-world embedded environments where privacy, energy efficiency, and communication constraints are simultaneously binding.},
  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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