Federated Machine Learning Outperforms Centralized Machine Learning for Fraud Detection: Evidence From 284,807 Real-World Transactions
Issues in Informing Science and Information Technology
• Volume 23
• 2026
• pp. 01
Aim/Purpose
This study investigates whether federated machine learning (FML) can match or exceed centralized machine learning (CML) performance for financial fraud detection while maintaining complete data privacy.
Background Financial fraud detection systems traditionally rely on CML, which aggregates sensitive transaction data from multiple institutions, raising privacy concerns and regulatory compliance challenges under the GDPR and CCPA.
Methodology
Using the Kaggle Credit Card Fraud dataset (284,807 transactions), this study compared identical neural network architectures in centralized and federated settings. The FML approach utilized the FedAvg algorithm across 10 clients with non-IID data distribution over 20 rounds.
Contribution
This research challenges the assumption that centralized learning out-performs federated approaches, providing the first comprehensive empirical comparison showing FML superiority on real-world fraud data.
Findings
FML outperformed CML in F1-score (0.7957 vs. 0.7411) and precision (0.8409 vs. 0.6587). While FML significantly reduces false positives and operational costs, CML remains competitive when fraud losses outweigh the costs of false positives.
Recommendations for Practitioners
Financial institutions should adopt FML for fraud detection when multi-institution collaboration or data-localization compliance is required. Its higher precision makes it well-suited for production environments focused on reducing false positives.
Recommendation for Researchers
Future research should examine advanced aggregation, differential privacy, and scalability. It should also analyze regularization effects to determine why FML achieves superior precision.
Impact on Society
This research shows that privacy preservation can enhance fraud detection, enabling multi-institution collaboration while protecting consumer data and reducing friction by reducing false positives.
Future Research
Future work should explore FML in real-time institutional settings, across diverse fraud contexts, and within integrated cross-domain frameworks such as credit risk assessment.
This study investigates whether federated machine learning (FML) can match or exceed centralized machine learning (CML) performance for financial fraud detection while maintaining complete data privacy.
Background Financial fraud detection systems traditionally rely on CML, which aggregates sensitive transaction data from multiple institutions, raising privacy concerns and regulatory compliance challenges under the GDPR and CCPA.
Methodology
Using the Kaggle Credit Card Fraud dataset (284,807 transactions), this study compared identical neural network architectures in centralized and federated settings. The FML approach utilized the FedAvg algorithm across 10 clients with non-IID data distribution over 20 rounds.
Contribution
This research challenges the assumption that centralized learning out-performs federated approaches, providing the first comprehensive empirical comparison showing FML superiority on real-world fraud data.
Findings
FML outperformed CML in F1-score (0.7957 vs. 0.7411) and precision (0.8409 vs. 0.6587). While FML significantly reduces false positives and operational costs, CML remains competitive when fraud losses outweigh the costs of false positives.
Recommendations for Practitioners
Financial institutions should adopt FML for fraud detection when multi-institution collaboration or data-localization compliance is required. Its higher precision makes it well-suited for production environments focused on reducing false positives.
Recommendation for Researchers
Future research should examine advanced aggregation, differential privacy, and scalability. It should also analyze regularization effects to determine why FML achieves superior precision.
Impact on Society
This research shows that privacy preservation can enhance fraud detection, enabling multi-institution collaboration while protecting consumer data and reducing friction by reducing false positives.
Future Research
Future work should explore FML in real-time institutional settings, across diverse fraud contexts, and within integrated cross-domain frameworks such as credit risk assessment.
federated learning, fraud detection, privacy-preserving ML, distributed learning, GDPR compliance, class imbalance, financial fraud
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