A Survey of Federated Learning for IoT: Addressing Resource Constraints and Heterogeneous Challenges

Sristi Vashisth, Anjali Goyal

Abstract


Federated Learning (FL) has emerged as a promising approach to address the challenges of data privacy, security, and scalability in Internet of Things (IoT) environments. This paper provides a comprehensive survey of recent advances in FL for resource-constrained IoT systems, focusing on addressing the challenges of heterogeneous data, limited computational resources, and dynamic network environments. The survey highlights key achievements, including accuracy improvements of over 90% in domains such as smart homes, industrial IoT, and healthcare. Furthermore, FL solutions leveraging edge and fog computing have demonstrated significant energy efficiency improvements, reducing power consumption by up to 30%. A comparative analysis of state-of-the-art FL frameworks is presented, identifying critical research gaps in scalability, adaptive frameworks, and the integration of blockchain for enhanced security. Finally, the paper proposes future research directions to develop robust, efficient, and scalable FL solutions tailored for diverse IoT applications.


Full Text:

PDF

References


Khan, L. U., Saad, W., Han, Z., Hossain, E., &

Hong, C. S. (2021). Federated learning for inter-

net of things: Recent advances, taxonomy, and

open challenges. IEEE Communications Surveys

& Tutorials, 23 (3), 1759-1799.

Rudraraju, S. R., Suryadevara, N. K., & Negi,

A. (2023). Heterogeneous sensor data acquisition

and federated learning for resource constrained

IoT devices—A validation. IEEE Sensors Jour-

nal, 23 (15), 17602-17610.

Imteaj, A., Mamun Ahmed, K., Thakker, U.,

Wang, S., Li, J., & Amini, M. H. (2022). Fed-

erated learning for resource-constrained IoT de-

vices: Panoramas and state of the art. Federated

and Transfer Learning, 7-27.

Ficco, M., Guerriero, A., Milite, E., Palmieri,

F., Pietrantuono, R., & Russo, S. (2024). Feder-

ated learning for IoT devices: Enhancing TinyML

with on-board training. Information Fusion, 104,

Gupta, D. N., Kumar, R., & Kumar, A. (2022).

Federated learning for IoT devices. In Federated

Learning for IoT Applications (pp. 19-29). Cham:

Springer International Publishing.

Imteaj, A., Mamun Ahmed, K., Thakker, U.,

Wang, S., Li, J., & Amini, M. H. (2022). Fed-

erated learning for resource-constrained IoT de-

vices: Panoramas and state of the art. Federated

and Transfer Learning, 7-27.

Reyes, J., Di Jorio, L., Low-Kam, C., & Kersten-

Oertel, M. (2021). Precision-weighted federated

learning. arXiv preprint arXiv:2107.09627.

Feraudo, A., Yadav, P., Safronov, V., Popescu,

D. A., Mortier, R., Wang, S., . . . & Crowcroft, J.

(2020, April). CoLearn: Enabling federated learn-

ing in MUD-compliant IoT edge networks. In Pro-

ceedings of the Third ACM International Work-

shop on Edge Systems, Analytics and Networking

(pp. 25-30).

Pfeiffer, K., Rapp, M., Khalili, R., & Henkel,

J. (2023). Federated learning for computation-

ally constrained heterogeneous devices: A survey.

ACM Computing Surveys, 55 (14s), 1-27.

Zhang, T., He, C., Ma, T., Gao, L., Ma, M.,

& Avestimehr, S. (2021, November). Federated

learning for internet of things. In Proceedings

of the 19th ACM Conference on Embedded Net-

worked Sensor Systems (pp. 413-419).

Sun, W., Lei, S., Wang, L., Liu, Z., & Zhang,

Y. (2020). Adaptive federated learning and digital

twin for industrial internet of things. IEEE Trans-

actions on Industrial Informatics, 17 (8), 5605-

Ibraimi, L., Selimi, M., & Freitag, F. (2021, De-

cember). BePOCH: Improving federated learning

Title of the. . . Informatica 47 (2023) 41–52 11

performance in resource-constrained computing

devices. In 2021 IEEE Global Communications

Conference (GLOBECOM) (pp. 1-6). IEEE.

Moore, E., Imteaj, A., Rezapour, S., & Amini, M.

H. (2023). A survey on secure and private feder-

ated learning using blockchain: Theory and appli-

cation in resource-constrained computing. IEEE

Internet of Things Journal.

Wang, S., Tuor, T., Salonidis, T., Leung, K. K.,

Makaya, C., He, T., & Chan, K. (2019). Adaptive

federated learning in resource constrained edge

computing systems. IEEE Journal on Selected

Areas in Communications, 37 (6), 1205-1221.

Nguyen, V. D., Sharma, S. K., Vu, T. X.,

Chatzinotas, S., & Ottersten, B. (2020). Efficient

federated learning algorithm for resource alloca-

tion in wireless IoT networks. IEEE Internet of

Things Journal, 8 (5), 3394-3409.

Wu, D., Ullah, R., Harvey, P., Kilpatrick, P.,

Spence, I., & Varghese, B. (2022). Fedadapt:

Adaptive offloading for IoT devices in federated

learning. IEEE Internet of Things Journal, 9 (21),

-20901.

Zhang, T., Gao, L., He, C., Zhang, M., Krishna-

machari, B., & Avestimehr, A. S. (2022). Feder-

ated learning for the internet of things: Applica-

tions, challenges, and opportunities. IEEE Inter-

net of Things Magazine, 5 (1), 24-29.

Chen, H., Huang, S., Zhang, D., Xiao, M.,

Skoglund, M., & Poor, H. V. (2022). Federated

learning over wireless IoT networks with opti-

mized communication and resources. IEEE Inter-

net of Things Journal, 9 (17), 16592-16605.

AbdulRahman, S., Tout, H., Mourad, A., &

Talhi, C. (2020). FedMCCS: Multicriteria client

selection model for optimal IoT federated learn-

ing. IEEE Internet of Things Journal, 8 (6), 4723-

Saha, R., Misra, S., & Deb, P. K. (2020). FogFL:

Fog-assisted federated learning for resource-

constrained IoT devices. IEEE Internet of Things

Journal, 8 (10), 8456-8463.

Wu, Q., He, K., & Chen, X. (2020). Personalized

federated learning for intelligent IoT applications:

A cloud-edge based framework. IEEE Open Jour-

nal of the Computer Society, 1, 35-44.

Ghimire, B., & Rawat, D. B. (2022). Recent ad-

vances on federated learning for cybersecurity and

cybersecurity for federated learning for internet of

things. IEEE Internet of Things Journal, 9 (11),

-8249.

Vashisth, S. (2024). Dynamic anomaly detection

in resource-constrained environments: Harness-

ing robust random cut forests for resilient cyber-

netic defense. Informatica, 48 (23).

Wang, H., Kaplan, Z., Niu, D., & Li, B. (2020,

July). Optimizing federated learning on non-iid

data with reinforcement learning. In IEEE INFO-

COM 2020-IEEE conference on computer com-

munications (pp. 1698-1707). IEEE.

Zhang, L., Lei, X., Shi, Y., Huang, H., & Chen,

C. (2023). Federated learning for IoT devices with

domain generalization. IEEE Internet of Things

Journal, 10 (11), 9622-9633.

He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang,

H., . . . & Avestimehr, S. (2020). Fedml: A re-

search library and benchmark for federated ma-

chine learning. arXiv preprint arXiv:2007.13518.

Bonawitz, K. (2019). Towards federated learn-

ing at scale: System design. arXiv preprint

arXiv:1902.01046.




DOI: https://doi.org/10.31449/inf.v49i17.7707

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.