FedAvg for Privacy in The UCI Heart Disease Dataset with Analyzing Communication-Computation Trade-offs

Abstract

Several studies use privacy-enhanced technologies (PETs) like federated learning (FL) for decentralized heart disease detection. However, conventional FL algorithms frequently suffer from model instability issues driven by dataset heterogeneity across medical institutions. This study implements a robust FL framework to address this heterogeneity while simultaneously improving communication cost efficiency within the standard federated averaging (FedAvg) protocol. We employ a deep learning neural network algorithm evaluated on the UCI Heart Disease dataset, which is distributed across multiple local clients in an independent and identically distributed (IID) manner to establish a controlled baseline. The proposed algorithm evaluates predefined local epoch (E) variations, precisely calculating the total communication costs required to reach convergence relative to conventional centralized training accuracy thresholds. Furthermore, we identify the optimal trade-off point between network communication costs and client hardware computational overhead by utilizing the multi-objective weighted-sum method (WSM). Empirical test results demonstrate that systematically increasing the local epoch configuration significantly reduces convergence communication costs, albeit at the expense of increased client computational load. The optimal architectural configuration is identified at E = 20, yielding a minimum balanced WSM score of 0.246. Other configurations cause severe system imbalance, heavily dominating either network communication cost or client computational load. Notably, our optimized approach achieves a 72.5% communication cost efficiency, significantly outperforming the 30% baseline established in prior research.