Cloud-Based Distributed Deep Learning for Credit Card Fraud Detection: A Scalability and Data Partitioning Analysis

Recent advances in artificial intelligence have significantly improved credit card fraud detection, with deep learning emerging as an effective approach for learning complex transaction patterns. However, as financial transaction datasets continue to grow, training deep learning models on a single computing node becomes increasingly computationally expensive, motivating the adoption of cloud-based distributed learning. Because distributed deep learning partitions training data across multiple worker nodes, this study evaluates how different data partitioning strategies influence predictive performance, computational efficiency, and scalability. The proposed framework was evaluated under both independently and identically distributed (IID) and non-independent and identically distributed (non-IID) data partitioning strategies, including random, stratified, label skew, quantity skew, temporal skew, and amount skew, using single-node, three-worker, and seven-worker configurations. Experimental results demonstrate a maximum training speedup of 4.181× and a 76.084% reduction in average epoch training time while maintaining consistently high recall across all partitioning strategies; however, the F1-score decreased from 0.696 to 0.587 (approximately 16%), due to increased false-positive predictions. The evaluated partitioning strategies exhibited different trade-offs between predictive performance and computational efficiency. These findings demonstrate that the proposed framework provides a scalable solution for cloud-based credit card fraud detection while offering practical insights into the influence of data partitioning strategies on distributed deep learning performance.