Federated learning model with dynamic scoring-based client selection for diabetes diagnosis
Shamim Ahmed, M. Shamim Kaiser, Sudipto Chaki, Saad Aloteibi, Mohammad Ali Moni
Abstract
Federated learning (FL) is a privacy-preserving paradigm in distributed machine learning that enables clients to collaboratively train models without sharing their raw data. However, the variety of client data, device setups, and network conditions provides serious difficulties for FL systems. Random client sampling in such environments often leads to suboptimal outcomes, including lower model accuracy, slower convergence rates, and reduced fairness. To address these issues, this study proposes a dynamic client selection mechanism based on a scoring system that evaluates clients based on three key parameters: accuracy, loss, and execution time. We propose a scoring-based framework for adaptive client selection in federated learning (FL) and implement it in an ML-driven diabetes detection system. Evaluations with 200 communication rounds demonstrate improved global and local model performance, faster convergence, and optimized resource utilization. The framework dynamically selects clients, improving execution efficiency and addressing key FL challenges, including data heterogeneity, fairness, communication overhead, and privacy. Our findings highlight its potential for scalable and efficient FL in healthcare applications while paving the way for future advancements in adaptive client selection.