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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Adinda Mariasti Dewi
Telkom University
Computer Science & IT Engineering

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Robust Aggregation Strategies in Federated Learning for Credit Risk Assessment Sulthonika Mahfudz Al Mujahidin; Michael Angello Qadosy Riyadi; Adinda Mariasti Dewi; Mustafa Kamal
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
A Comparative Evaluation of Federated Learning Algorithms for Privacy-Preserving Academic Prediction on Heterogeneous Data Michael Angello Qadosy Riyadi; Adinda Mariasti Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7288

Abstract

The rapid growth of educational data enables predictive analytics for academic performance, yet privacy regulations like GDPR and FERPA severely restrict centralized data sharing. Although Federated Learning (FL) has succeeded in privacy-sensitive fields such as healthcare, its application in education remains underexplored, lacking systematic comparative studies of multiple FL algorithms across diverse educational datasets—especially emphasizing recall and ROC-AUC as critical metrics for early identification of students at academic risk. This study fills this gap by evaluating five FL algorithms—Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Dynamics (FedDyn), Fair Federated Averaging (q-FedAvg), and SCAFFOLD—for privacy-preserving prediction of academic outcomes. Three public datasets were purposefully selected for their representativeness and heterogeneity: Predict Students Dropout and Academic Success (binary dropout prediction with socioeconomic factors), Student Performance (multi-class grade prediction in secondary education), and xAPI-Edu-Data (multi-class performance based on online learning activities). Local neural networks employed Stratified 5-Fold Cross-Validation, while FL algorithms ran for 50 communication rounds. Global models, particularly q-FedAvg and FedProx, consistently surpassed local models, with q-FedAvg achieving 0.7668 accuracy, 0.6813 recall, and 0.8810 ROC-AUC on Predict Students Dropout; 0.8580 accuracy and 0.9871 recall on Student Performance; and 0.7396 accuracy and 0.8815 ROC-AUC on xAPI-Edu-Data. Paired T-tests confirmed significant recall gains for most global models (p < 0.05). These results highlight FL’s ability to handle data heterogeneity and privacy constraints while improving predictive performance, thereby supporting timely educational interventions and enhanced student retention policies.
Co-Authors Michael Angello Qadosy Riyadi Mustafa Kamal Sulthonika Mahfudz Al Mujahidin