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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Riyadi, Michael Angello Qadosy
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Computer Science & IT Engineering

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Robust Aggregation Strategies in Federated Learning for Credit Risk Assessment Mujahidin, Sulthonika Mahfudz Al; Riyadi, Michael Angello Qadosy; Dewi, Adinda Mariasti; Kamal, Mustafa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7133

Abstract

Financial institutions face challenges in credit risk assessment due to fragmented data and strict privacy regulations, which hinder predictive modeling and increase financial risks. Federated Learning (FL) enables privacy-preserving collaborative modeling without sharing raw data. This study evaluates five FL aggregation methods—Federated Averaging (FedAvg), Weighted Average, Median Aggregation, Federated Proximal (FedProx), and Stochastic Controlled Averaging (SCAFFOLD)—using logistic regression on the Credit Approval dataset (690 records, five clients) with non-IID label and feature distributions. Local models were trained and aggregated over 50 rounds. Median Aggregation outperformed the other methods, achieving an F1-score of 97.85% and a recall of 80.6% (vs. 72.3% for others), demonstrating robustness against data skewness. However, global model performance (85.22% for FedAvg, Weighted Average, FedProx, SCAFFOLD; 85.80% for Median) remained static across rounds, indicating limited convergence due to rapid local model convergence and non-IID challenges. The high communication cost of 50 rounds highlights a trade-off between accuracy and efficiency, necessitating optimized strategies like adaptive regularization or client sampling. This study advances theoretical understanding of FL under heterogeneity and provides practical guidance for secure, regulation-compliant credit risk modeling in financial institutions. Future work should explore larger datasets, multi-round convergence, and privacy mechanisms like differential privacy to mitigate risks such as model inversion attacks while ensuring compliance
Comparative Evaluation of Federated Learning Algorithms in Dirichlet Non-IID Medical Imaging Riyadi, Michael Angello Qadosy; Dewi, Adinda Mariasti; Mukhlishin, Zahid Abdullah Nur; Arep, Zalsabilah Rezky Amelia
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 1 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i1.44597

Abstract

Machine learning has achieved diagnostic performance comparable to clinical experts on medical imaging, yet centralized training paradigms necessitate patient data aggregation, risking violations of privacy regulations such as GDPR and HIPAA. In 2023, 1,853 healthcare data breaches were reported in the United States, compromising over 133 million medical records, rendering raw inter-institutional data exchange increasingly unsustainable. Federated Learning (FL) offers a viable solution by enabling collaborative model training without data transfer. However, prior studies predominantly evaluate single algorithms and often neglect non-IID Dirichlet-distributed conditions and probabilistic calibration metrics like log-loss. This study rigorously compares FedAvg, FedProx, FedSVRG, and FedAtt across three MedMNIST v2 datasets—PneumoniaMNIST (binary), DermaMNIST, and BloodMNIST (multi-class)—using three clients under non-IID Dirichlet partitioning (α=0.1) over 50 communication rounds. FedProx demonstrates the most consistent performance and stability, achieving accuracy of 0.9521 and log-loss of 0.1850 on PneumoniaMNIST; 0.8595 and 0.4066 on BloodMNIST; and 0.5747 and 1.5996 on DermaMNIST. It also exhibits fastest convergence and superior probability calibration. Thus, FedProx’s proximal regularization enhances FL robustness against extreme clinical heterogeneity, establishing it as a scalable, privacy-preserving framework for cross-institutional medical image diagnostics.
Co-Authors Arep, Zalsabilah Rezky Amelia Dewi, Adinda Mariasti Mujahidin, Sulthonika Mahfudz Al Mukhlishin, Zahid Abdullah Nur Mustafa Kamal