Journal of Resource Management and Decision Engineering 4 Serial Number 10 2025 03 01 Optimizing Federated Learning Performance in Heterogeneous Distributed Systems from Computational Resource and Data Distribution Perspectives Optimizing Federated Learning Performance in Heterogeneous Distributed Systems from Computational Resource and Data Distribution Perspectives 1 9 EN Journal Article 2024 09 04 <p>This study aims to develop an adaptive resource allocation framework that simultaneously optimizes computational efficiency and data distribution heterogeneity in federated learning (FL) systems. A simulation-based experimental design evaluated FL performance across 50 heterogeneous client nodes (2-core to 32-core CPUs) under three resource allocation strategies (homogeneous, heterogeneous, adaptive) and three data skew levels (IID, moderate non-IID, severe non-IID). The framework utilized TensorFlow Federated for FL orchestration, Prometheus/Grafana for real-time resource monitoring, and a custom Python module for data skew quantification. Metrics included global accuracy, communication rounds, energy consumption, and convergence dynamics across CIFAR-10, FEMNIST, and medical imaging datasets. Adaptive allocation reduced communication rounds by 37.2% (vs. homogeneous) while maintaining 94.3% target accuracy under severe non-IID conditions (Gini &gt;0.7), outperforming homogeneous (78.2% accuracy) and heterogeneous (85.1% accuracy) strategies. It achieved a 28.7% system-wide energy reduction by capping edge-node compute at 55% utilization (preventing underutilization) and prioritizing cloud-node bandwidth (avoiding overutilization). The framework eliminated straggler effects (3.2% vs. 18.3% in homogeneous allocation) and demonstrated a 12.7% accuracy advantage over heterogeneous allocation when data skew exceeded Gini coefficient 0.6. Statistical validation confirmed significant interaction effects between resource allocation and data skew (F = 142.7, p &lt; 0.001). This work establishes that intelligent resource orchestration—dynamically adjusting compute quotas based on real-time node performance and data skew—resolves the interdependent challenges of computational heterogeneity and non-IID data in FL. The adaptive framework delivers multiplicative gains in accuracy (up to 13.1%), communication efficiency (37.2% fewer rounds), and energy conservation (28.7% less consumption), providing a scalable blueprint for real-world FL deployment in healthcare, finance, and IoT.</p> https://journalrmde.com/index.php/jrmde/article/download/401/369