Towards Explainable and Balanced Federated Learning: A Neural Network Approach for Multi-Client Fraud Detection

The growing demand for secure and privacy-preserving machine learning frameworks has resulted in the implementation of federated learning (FL), especially in critical areas like Credit card fraud detection. This study presents a comprehensive federated learning architecture that incorporates Neural Networks as local models, in conjunction with KMeans-SMOTEENN to address class imbalance in distributed datasets. The system utilises the Flower framework, employing the FedAvg algorithm across ten decentralised clients to collectively train the global model while preserving raw data confidentiality. To improve model transparency and cultivate stakeholder trust, Local Interpretable Model-Agnostic Explanations (LIME) is utilized, offering localised, comprehensible insights into model decisions. The experimental results indicate that the suggested method effectively achieves high predictive accuracy and explainability, rendering it appropriate for real-world fraud detection contexts that necessitate data confidentiality and model accountability.

@article{Damanik2025,
  title     = {Towards Explainable and Balanced Federated Learning: A Neural Network Approach for Multi-Client Fraud Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nurafni Damanik and Chuan-Ming Liu},
  doi       = {10.14569/IJACSA.2025.0160837},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160837}
}