Particle Swarm Optimization for Hyperparameter Tuning in  FedProx-Based Federated Learning Using DenseNet-201  for Breast Cancer Classification

Rizal Dwi Anggoro; Winarno Winarno; Ery Permana Yudha

doi:10.52436/1.jutif.2026.7.4.6012

Authors

Rizal Dwi Anggoro Informatics, Universitas Sebelas Maret, Indonesia
Winarno Informatics, Universitas Sebelas Maret, Indonesia
Ery Permana Yudha Informatics, Universitas Sebelas Maret, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.4.6012

Keywords:

Breast cancer classification, DenseNet-201, Federated learning, Federated proximal, Non-IID, Particle swarm optimization

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, making early detection a critical clinical priority. While deep learning has demonstrated strong performance in mammogram classification, its development is constrained by data privacy regulations that prevent centralized data collection across medical institutions. Federated learning (FL) offers a promising solution by enabling distributed model training without transferring raw patient data. However, FL faces significant challenges under Non-Independent and Identically Distributed (Non-IID) data conditions, which are common in real-world medical settings and can degrade model performance and convergence stability. This study proposes a breast cancer classification system based on FL using FedProx and DenseNet-201, with hyperparameter optimization via Particle Swarm Optimization (PSO) to improve model performance under heterogeneous data distributions. Experiments were conducted across multiple Dirichlet distribution scenarios (α = 0.1, 0.3, 0.5) comparing FL baseline and FL PSO based. Results show that PSO consistently improved performance across all scenarios, with the most significant gain observed under highly Non-IID conditions (α = 0.1), where the F1-score increased from 83.18% to 88.00%. PSO-optimized FL also demonstrated faster convergence, reducing the number of rounds required to reach optimal performance. Furthermore, the optimized configuration yielded lower training time per round compared to the baseline. These findings indicate that PSO-based hyperparameter optimization effectively enhances FL performance under data heterogeneity while preserving patient data privacy.

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References

J. Ferlay, M. E. Rebecca, and L. S. Mph, “Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 74, February, pp. 229–263, 2024, doi: 10.3322/caac.21834.

Widyawati, “Kanker Payudara Paling Banyak di Indonesia, Kemenkes Targetkan Pemerataan Layanan Kesehatan,” Kementerian Kesehatan Republik Indonesia. Accessed: May 10, 2026. [Online]. Available: https://kemkes.go.id/id/kanker-payudaya-paling-banyak-di-indonesia-kemenkes-targetkan-pemerataan-layanan-kesehatan

M. Marcon, M. H. Fuchsjäger, P. Clauser, and R. M. Mann, “ESR Essentials: screening for breast cancer - general recommendations by EUSOBI,” European Radiology, vol. 34, no. 10, pp. 6348–6357, 2024, doi: 10.1007/s00330-024-10740-5.

S. Hussain, M. Ali, F. Ali Pirzado, M. Ahmed, and J. G. Tamez-Peña, “Comparative Analysis of Deep Learning Models for Breast Cancer Classification on Multimodal Data,” in Proceedings of the First International Workshop on Vision-Language Models for Biomedical Applications, in VLM4Bio’24. New York, NY, USA: Association for Computing Machinery, 2024, pp. 31–39. doi: 10.1145/3689096.3689462.

B. Sistaninejhad, H. Rasi, and P. Nayeri, “A Review Paper about Deep Learning for Medical Image Analysis,” Computational and Mathematical Methods in Medicine, vol. 2023, no. 1, p. 7091301, Jan. 2023, doi: 10.1155/2023/7091301.

“UU No. 27 Tahun 2022.” Accessed: May 10, 2026. [Online]. Available: https://peraturan.bpk.go.id/Details/229798/uu-no-27-tahun-2022

E. Union, General Data Protection Regulation (GDPR). 2016.

N. Rieke et al., “The future of digital health with federated learning.,” NPJ Digital Medicine, vol. 3, p. 119, 2020, doi: 10.1038/s41746-020-00323-1.

B. Yurdem, M. Kuzlu, M. K. Gullu, F. O. Catak, and M. Tabassum, “Federated learning: Overview, strategies, applications, tools and future directions,” Heliyon, vol. 10, no. 19, p. e38137, Oct. 2024, doi: 10.1016/J.HELIYON.2024.E38137.

P. Kairouz et al., “Advances and Open Problems in Federated Learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021, doi: 10.1561/2200000083.

M. Haller, C. Lenz, R. Nachtigall, F. M. Awayshehl, and S. Alawadi, “Handling Non-IID Data in Federated Learning: An Experimental Evaluation Towards Unified Metrics,” 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Tec, pp. 762–770, 2023, doi: 10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361408.

Y. Shi, Y. Zhang, Y. Xiao, and L. Niu, “Optimization Strategies for Client Drift in Federated Learning: A review,” Procedia Computer Science, vol. 214, no. C, pp. 1168–1173, Jan. 2022, doi: 10.1016/J.PROCS.2022.11.292.

P. Qi, D. Chiaro, A. Guzzo, M. Ianni, G. Fortino, and F. Piccialli, “Model aggregation techniques in federated learning: A comprehensive survey,” Future Generation Computer Systems, vol. 150, pp. 272–293, Jan. 2024, doi: 10.1016/J.FUTURE.2023.09.008.

X. Mu et al., “FedProc: Prototypical contrastive federated learning on non-IID data,” Future Generation Computer Systems, vol. 143, pp. 93–104, Jun. 2023, doi: 10.1016/J.FUTURE.2023.01.019.

P. Prathusha and K. Aparna, “A Comparative Analysis of FedAvg, FedProx, and Scaffold in Gait-Based Activity Recognition by Evaluating Accuracy, Privacy, and Explainability,” Global Journal of Engineering Innovations and Interdisciplinary Research, vol. 5, no. 1, 2025, doi: 10.33425/3066-1226.1067.

M. Kundroo and T. Kim, “Federated learning with hyper-parameter optimization,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 9, p. 101740, 2023, doi: https://doi.org/10.1016/j.jksuci.2023.101740.

M. A. P. Putra, “Enhancing Federated Learning Performance through Adaptive Client Optimization with Hyperparameter Tuning,” Journal of Applied Data Sciences, vol. 5, no. 2, pp. 747–755, May 2024, doi: 10.47738/jads.v5i2.251.

M. Güler, G. Sart, Ö. Algorabi, A. N. Adıguzel Tuylu, and Y. S. Türkan, “Breast Cancer Classification with Various Optimized Deep Learning Methods,” Diagnostics, vol. 15, no. 14, p. 1751, Jul. 2025, doi: 10.3390/DIAGNOSTICS15141751.

S. Akhter and J. Liu, “Comprehensive Performance Evaluation of Deep Learning Models for Optimizing Breast Cancer Classification on Mammogram Datasets,” 2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025, pp. 197–204, 2025, doi: 10.1109/AIIoT65859.2025.11105244.

M. F. Almufareh, N. Tariq, M. Humayun, and B. Almas, “A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework,” Healthcare (Switzerland), vol. 11, no. 24, pp. 1–31, 2023, doi: 10.3390/healthcare11243185.

G. Drainakis, P. Pantazopoulos, K. V Katsaros, V. Sourlas, A. Amditis, and D. I. Kaklamani, “From centralized to Federated Learning: Exploring performance and end-to-end resource consumption,” Computer Networks, vol. 225, p. 109657, 2023, doi: https://doi.org/10.1016/j.comnet.2023.109657.

P. R. Lorenzo, J. Nalepa, M. Kawulok, L. S. Ramos, and J. R. Pastor, “Particle swarm optimization for hyper-parameter selection in deep neural networks,” in Proceedings of the Genetic and Evolutionary Computation Conference, in GECCO ’17. New York, NY, USA: Association for Computing Machinery, 2017, pp. 481–488. doi: 10.1145/3071178.3071208.

A.-H. M. Emara, G. Atteia, and J. H. Alkhateeb, “Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm,” IEEE Access, vol. 13, pp. 134506–134518, 2025, doi: 10.1109/ACCESS.2025.3591403.

K. B. Aqdar et al., “Mammogram mastery: A robust dataset for breast cancer detection and medical education,” Data Brief, vol. 55, p. 110633, Aug. 2024, doi: 10.1016/J.DIB.2024.110633.

W. Xie, R. Xiong, J. Zhang, J. Jin, and J. Luo, “Federated variational generative learning for heterogeneous data in distributed environments,” Journal of Parallel and Distributed Computing, vol. 191, p. 104916, Sep. 2024, doi: 10.1016/J.JPDC.2024.104916.

H. Chen and H. Vikalo, “Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2023-June, pp. 5027–5036, Jun. 2022, doi: 10.1109/CVPRW59228.2023.00531.

V. W. Lumumba, D. Kiprotich, M. L. Mpaine, N. G. Makena, and M. D. Kavita, “Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models,” American Journal of Theoretical and Applied Statistics, vol. 13, no. 5, pp. 127–137, Oct. 2024, doi: 10.11648/J.AJTAS.20241305.13.

M. Ye, X. Fang, B. Du, P. C. Yuen, and D. Tao, “Heterogeneous Federated Learning: State-of-the-art and Research Challenges,” ACM Computing Surveys, vol. 56, no. 3, pp. 1–44, Mar. 2024, doi: 10.1145/3625558.

R. Sharma, J. S. Matharu, and K. S. Parmar, “A survey on Particle Swarm Optimization: Evolution, adaptations and practical implementations,” Applied Soft Computing, vol. 186, p. 114016, Jan. 2026, doi: 10.1016/J.ASOC.2025.114016.

D. Freitas, L. G. Lopes, and F. Morgado-Dias, “Particle Swarm Optimisation: A Historical Review Up to the Current Developments,” Entropy, vol. 22, no. 3, p. 362, Mar. 2020, doi: 10.3390/E22030362.

J. Opitz, “A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice,” Transactions of the Association for Computational Linguistics, vol. 12, pp. 820–836, Dec. 2024, doi: 10.1162/TACL_A_00675/122720.

A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, Jan. 2021, doi: 10.1016/j.aci.2018.08.003.

S. Khan et al., “Bilevel Hyperparameter Optimization and Neural Architecture Search for Enhanced Breast Cancer Detection in Smart Hospitals Interconnected With Decentralized Federated Learning Environment,” IEEE Access, vol. 12, pp. 63618–63628, 2024, doi: 10.1109/ACCESS.2024.3392572.

E. H. Houssein and A. Sayed, “Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices,” Computers in Biology and Medicine, vol. 163, p. 107195, Sep. 2023, doi: 10.1016/J.COMPBIOMED.2023.107195.