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REFERENCES

1 
X. Fan, Y. Wang, Y. Huo, and Z. Tian, ``1-Bit compressive sensing for efficient federated learning over the air,'' IEEE Transactions on Wireless Communications, vol. 22, no. 3, pp. 2139-2155, 2023.DOI
2 
N. Shi and R. A. Kontar, ``Personalized federated learning via domain adaptation with an application to distributed 3D printing,'' Technometrics, vol. 65, no. 3, pp. 328-339, 2023.DOI
3 
M. F. Pervej, R. Jin, and H. Dai, ``Resource constrained vehicular edge federated learning with highly mobile connected vehicles,'' IEEE Journal on Selected Areas in Communications, vol. 41, no. 6, pp. 1825-1844, 2023.DOI
4 
Y. Zou, Z. Wang, and X. Chen, ``Knowledge-guided learning for transceiver design in over-the-air federated learning,'' IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 270-285, 2023.DOI
5 
X. Deng, J. Li, and C. Ma, ``Low-latency federated learning with DNN partition in distributed industrial IoT networks,'' IEEE Journal on Selected Areas in Communications, vol. 41, no. 3, pp. 755-775, 2023.DOI
6 
Y. Jin, Z. You, and N. Cai, ``Simplified inception module based Hadamard attention mechanism for medical image classification,'' Computer and Communication, vol. 11, no. 6, pp. 1-18, 2023.DOI
7 
R. N. Abirami, P. M. D. R. Vincent, and V. Rajinikanth, ``COVID-19 classification using medical image synthesis by generative adversarial networks,'' International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS), vol. 30, no. 3, pp. 385-401, 2022.DOI
8 
Y. Kurmi and V. Chaurasia, ``Classification of Magnetic Resonance Images for Brain Tumor Detection,'' IET Image Processing, vol. 14, no. 12, pp. 2808-2818, 2020.DOI
9 
Priyanka and D. Kumar, ``Meta-heuristic Techniques to Train Artificial Neural Networks for Medical Image Classification: A Review,'' Recent Advances in Computer Science and Communications, vol. 15, no. 4, pp. 513-530, 2022.DOI
10 
D. P. P. Envelope and M. W. Envelope, ``A hybridization of distributed policy and heuristic augmentation for improving federated learning approach,'' Neural Networks, vol. 146, no. 7, pp. 130-140, 2022.DOI
11 
Y. Kweon, B. Sun, and B. B. Park, ``Preserving privacy with federated learning in route choice behavior modeling,'' Transportation Research Record, vol. 2675, no. 10, pp. 268-276, 2021.DOI
12 
T. T. Vu, D. T. Ngo, and N. H. Tran, ``Cell-Free Massive MIMO for Wireless Federated Learning,'' IEEE Transactions on Wireless Communications, vol. 19, no. 10, pp. 6377-6392, 2020.DOI
13 
H. Shao and D. Zhong, ``Towards privacy palmprint recognition via federated hash learning,'' Electronics Letters, vol. 56, no. 25, pp. 1418-1420, 2020.DOI
14 
E. Elyan, P. Vuttipittayamongkol, and P. Johnston, ``Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward,'' Artificial Intelligence Surgery, vol. 2, no. 1, pp. 24-45, 2022.DOI
15 
S. Guo, K. Zhang, and B. Gong, ``Sandbox computing: A data privacy trusted sharing paradigm via blockchain and federated learning,'' IEEE Transactions on Computers, vol. 72, no. 3, pp. 800-810, 2023.DOI
16 
N. K. Ray, D. Puthal, and D. Ghai, ``Federated learning,'' IEEE Consumer Electronics Magazine, vol. 10, no. 6, pp. 106-107, 2021.DOI
17 
J. Zhao, X. Chang, and Y. Feng, ``Participant selection for federated learning with heterogeneous data in intelligent transport system,'' IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 1106-1115, 2023.DOI
18 
Y. M. Saputra, D. T. Hoang, and D. N. Nguyen, ``Dynamic federated learning-based economic framework for Internet-of-Vehicles,'' IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2100-2115, 2023.DOI
19 
H. Zhu, J. Kuang, and M. Yang, ``Client selection with staleness compensation in asynchronous federated learning,'' IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 4124-4129, 2023.DOI
20 
Z. Liu, S. Chen, and J. Ye, ``DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning,'' Journal of Supercomputing, vol. 79, no. 3, pp. 2819-2849, 2023.DOI
21 
M. Wasilewska, H. Bogucka, and H. V. Poor, ``Secure federated learning for cognitive radio sensing,'' IEEE Communications Magazine, vol. 61, no. 3, pp. 68-73, 2023.DOI
22 
J. D. Fernandez, S. Potenciano, and C. M. Lee, ``Privacy-preserving federated learning for residential short-term load forecasting,'' Applied Energy, vol. 326, no. 15, pp. 174-187, 2022.DOI
23 
S. N. Amin, P. Shivakumara, and T. X. Jun, ``An augmented reality-based approach for designing interactive food menu of restaurant using Android,'' Artificial Intelligence and Applications, vol. 1, no. 1, pp. 26-34, 2023.DOI
24 
H. Liu, X. Yuan, and Y. J. A. Zhang, ``CSIT-free model aggregation for federated edge learning,'' IEEE Wireless Communications Letters, vol. 10, no. 11, pp. 2440-2444, 2021.DOI
25 
J. Y. Park and J. G. Ko, ``FedHM: Practical federated learning for heterogeneous model deployments,'' ICT Express, vol. 10, no. 2, pp. 387-392, 2024.DOI