Federated Adaptive Personalized Optimization (Fed-APO): A Meta-Learning Approach to Enhancing Healthcare for Non-IID Multi-Healthcare Data
DOI:
https://doi.org/10.22399/ijcesen.1646Keywords:
Federated Learning, Healthcare, Non-IID Data, Meta-Learning, Adaptive OptimizationAbstract
Federated Learning (FL) becomes a progressive solution enabling confidential model collaboration training in healthcare settings. FL algorithms encounter significant limitations from medical data structures that maintain non-independent distributed characteristics thus leading to inefficient models while slowing down training time. The researchers introduce Fed-APO as their new framework that uses meta-learning aggregation techniques with individualized training protocols to improve the performance of FL systems working with diverse healthcare data. Fed-APO carries out model updates through individualized characteristics of clients to optimize global-local collaboration by implementing learning rate adaptation and weight adaptation methods. The proposed method receives evaluation from experimental tests performed on medical datasets featuring different attributes for heart disease prognosis and general health monitoring along with cancer identification. The research outcomes demonstrate that Fed-APO provides superior performance compared to standard FL techniques Fed-Avg and Fed-NOVA through its advanced accuracy levels and F1-score capabilities and decreased communication requirements. Fed-APO elevates accuracy by 5.55% more than Fed-Avg and 4.26% better than Fed-NOVA when communication rounds decrease by 16.7%. The Fed-APO system achieves exceptional performance scalability in medical applications through its multidimensional organization model training method which safeguards patient privacy at all times
References
[1] Cetinkaya, A. E., Akin, M., & Sagiroglu, S. (2021). Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation. IEEE 14th International Conference on Information Security and Cryptology, https://doi.org/10.1109/iscturkey53027.2021.9654356
[2] Efthymiadis, F., Karras, A., Karras, C., & Sioutas, S. (2024). Advanced optimization techniques for federated learning on Non-IID data. Future Internet, 16(10), 370. https://doi.org/10.3390/fi16100370
[3] Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions. (n.d.). Retrieved February 16, 2025, from https://arxiv.org/html/2411.12377
[4] Xie, L., Lin, M., Luan, T., Li, C., Fang, Y., Shen, Q., & Wu, Z. (2024). MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.06822
[5] Yang, L., Huang, J., Lin, W., & Cao, J. (2022). Personalized federated learning on Non-IID data via group-based meta-learning. ACM Transactions on Knowledge Discovery From Data, 17(4), 1–20. https://doi.org/10.1145/3558005
[6] Nguyen, D. C., Pham, Q., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O. A., & Hwang, W. (2021). Federated Learning for Smart Healthcare: a survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2111.08834
[7] Xu, Z., Zhang, Y., & Chen, X. (2021). Federated learning for electronic health records: A systematic review. Journal of Medical Internet Research, 23(12), e30832.
[8] Dayan, O., Roth, H. R., Zhong, Z., Harouni, A., Gentili, A., Abidin, A. Z., ... & Summers, R. M. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27(10), 1735-1743.
[9] Ali, A., Khan, A., & Mahmood, S. (2024). Challenges and solutions for federated learning in healthcare: A systematic review. Journal of Healthcare Engineering,
[10] Chen, Y., Liu, Y., & Wang, Z. (2023). Meta-learning with elastic constraints for personalized federated learning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 11425-11433.
[11] Alsulaimawi, M. (2024). Meta-FL: Optimization-based meta-aggregation for personalized federated learning. IEEE Transactions on Neural Networks and Learning Systems, In Press.
[12] Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., & Smith, V. (2021). Adaptive federated optimization. Proceedings of the International Conference on Machine Learning (ICML), 8864–8874.
[13] Liu, X., Zhang, L., & Li, H. (2022). Online meta-learning for aggregation optimization in federated learning. Neural Information Processing Systems (NeurIPS), 35, 23783-23795
[14] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 1273-1282.
[15] Wang, J., Liu, Q., Liang, H., Joshi, G., & Poor, H. V. (2020). Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in Neural Information Processing Systems (NeurIPS), 33, 7611-7623.
[16] Statlog (Heart) [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C57303.
[17] Tucker, Jay (2024), “SymbiPredict”, Mendeley Data, V1, doi: 10.17632/dv5z3v2xyd.1
[18] Rabie El Kharoua. (2024). Cancer Prediction Dataset. [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/8650621
[19] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
[20] Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S., 2019. “Federated learning with personalization layers,” arXiv preprint arXiv:1912.00818, 2019.
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