Rachmad Andri Atmoko, Sholeh Hadi Pramono, Muhammad Fauzan Edy Purnomo, Panca Mudjirahardjo, Mahdin Rohmatillah, Cries Avian
Federated learning faces challenges in explainability and trust when aggregating models from heterogeneous nodes with non-IID data distributions. This study presents FedXChain, a framework that combines privacy-preserving Federated-SHapley Additive exPlanations (SHAP) aggregation with Node-Specific Divergence Scores (NSDS) to quantify local explanation fidelity, adaptive trust-based aggregation, and blockchain-verified audit trails for transparent and verifiable collaboration. It validates FedXChain across three fundamentally different model architectures (Logistic Regression, Multi-Layer Perceptron, and Random Forest) on real-world medical data from the Wisconsin Breast Cancer dataset (569 clinical breast tissue samples). The experimental results show that FedXChain achieves 96.50% accuracy with excellent statistical reproducibility (CV < 2% across 5 independent runs). FedXChain also provides NSDS-based interpretability tracking, with observed NSDS values ranging from 0.1926 to 0.5768 across the evaluated architectures, supporting the analysis of explanation divergence under heterogeneous clients. In the final-round comparison, FedXChain reaches 96.5% accuracy under non-IID settings (α = 0.3), outperforming FedProx (89.5%, non-IID α = 0.5) and remaining competitive with FedAvg under IID conditions (96.0%). © (2026), (Dr D. Pylarinos). All rights reserved.
Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia; Faculty of Vocational Studies, Universitas Brawijaya, Malang, Indonesia; Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia
