A Decentralized Learning Architecture for Medical Prescription Anomaly Detection via Hybrid Federated-Swarm Learning

Abstract

The increasing use of electronic medical records (EMRs) has improved efficiency, accuracy, and accessibility of patient data. However, conventional centralized architectures suffer from single points of failure and data privacy issues. To address these challenges, this study proposes a decentralized machine learning architecture that combines concepts from Federated Learning (FL) and Swarm Learning (SL) for anomaly detection in medical prescriptions. The proposed architecture leverages blockchain and the InterPlanetary File System (IPFS) to enable secure model sharing and decentralized storage, thereby reducing communication complexity and establishing a transparent, decentralized parameter repository. Experimental evaluations were conducted using logistic regression (LR), a multi-layer perceptron (MLP), and a decision tree (DT) model. Compared with the FL baseline, the proposed system achieved superior efficiency, lower resource consumption, and improved latency, along with smaller block sizes. It, however, exhibited slightly lower transaction throughput and longer training rounds, reflecting the added complexity of decentralization. In predictive performance on the anomaly classification task, DT achieved the highest precision and recall under the evaluated dataset (F1-score=0.9912), followed by MLP (0.5504) and LR (0.2442). The decentralized training approach led to negligible performance loss relative to centralized models, less than 4% for LR and below 1% for both MLP and DT. Overall, the proposed system demonstrates a robust and efficient alternative for decentralized learning in healthcare applications, maintaining strong predictive performance while enhancing architectural transparency.