FedProx Optimization Based on Compression and Quantization for Efficient Heart Disease Prediction on Edge Devices

Abstract

Cardiovascular disease remains a leading cause of mortality worldwide, posing challenges for early detection within privacy-sensitive medical environments. Federated Learning (FL) offers a promising solution by enabling distributed model training without transferring raw patient data to a central server. However, FL deployment on edge devices is constrained by limited resources, system heterogeneity, and non-IID data, which degrade model stability and convergence. This study integrates sparsification-based compression and 16-bit quantization into FedProx and evaluates their combined and individual effects on heart disease prediction under compounded non-IID and straggler conditions. Using the Heart Disease Train-Test dataset from the UCI repository (1,025 samples, 14 attributes) and a KDD methodology, experiments simulated a non-IID FL environment with 10 clients and 30% stragglers over 50 rounds, averaged over five seeds. Results show that standard FedProx achieves the highest mean performance (86.44% accuracy, 87.34% F1-Score), closely followed by FedAvg (86.15%, 87.18%), while the proposed method attains a lower mean accuracy (84.88%) and F1-Score (85.87%), not statistically significant (p = 0.061, 0.098). ROC-AUC, however, is significantly lower for the proposed method (93.52% vs. 94.42%, p < 0.01), indicating reduced discriminative ability. An ablation study shows compression, not quantization, primarily drives the efficiency gains, reducing transmitted parameter size by 45.09% and accelerating convergence to 20.2 rounds compared with the baseline methods. These findings indicate that integrating compression and quantization into FedProx achieves substantial communication efficiency and faster convergence with only minor performance trade-offs, offering a viable option for resource-constrained edge deployments.