This systematic literature review uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to assess progress in blockchain-based Federated learning (FL) and Machine Learning (ML) for detecting financial fraud over the last five years (2020–2025). An initial pool of 29,274 records identified across IEEE Xplore, ACM Digital Library, and ScienceDirect yielded 1585 peer-reviewed studies that met the inclusion criteria. Both qualitative and quantitative approaches were used. The examined papers were classified according to algorithm type, fraud types, and evaluation measures. Credit card fraud and cryptocurrency fraud dominated the literature, with supervised learning (e.g., XGBoost, 95% accuracy) and federated learning (e.g., FedAvg, 91% accuracy) emerging as dominant methodologies. Centralized ML outperforms FL in latency but poses privacy risks. FL–blockchain hybrids reduce false positives. While precision, recall, and F1-score are commonly used, few studies use cost-sensitive criteria. Future research should prioritize adaptive FL aggregation, privacy-preserving ML, and cross-industry collaboration.

The deployment of deep learning models on resource-constrained embedded platforms presents significant challenges due to limited computational power, memory, and energy efficiency. To address this issue, this study proposes a novel quantization method tailored to accelerate object detection using a quantized version of the YOLOv5m model, called Q_YOLOv5m. This method reduces the model's computational complexity and memory footprint, allowing for faster inference and lower power consumption, making it ideal for real-time applications on embedded systems. This approach incorporates advanced weight and activation quantization techniques to balance performance with accuracy, dynamically adjusting precision based on hardware capabilities. The efficacy of Q_YOLOv5m was confirmed, exhibiting substantial enhancements in inference speed and a reduction in model size with negligible loss in object detection accuracy. The findings underscore the capability of Q_YOLOv5m for edge applications, including autonomous vehicles, intelligent surveillance, and IoT-based monitoring systems.