FedXChain: Explainable Federated Learning with Adaptive Trust Scoring and Blockchain-Based Audit Trails

Rachmad Andri Atmoko; Sholeh Hadi Pramono; Muhammad Fauzan Edy Purnomo; Panca Mudjirahardjo; Mahdin Rohmatillah; Cries Avian

doi:10.48084/etasr.15817

Authors

Rachmad Andri Atmoko Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia | Faculty of Vocational Studies, Universitas Brawijaya, Malang, Indonesia
Sholeh Hadi Pramono Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia
Muhammad Fauzan Edy Purnomo Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia
Panca Mudjirahardjo Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia
Mahdin Rohmatillah Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia
Cries Avian Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia

Volume: 16 | Issue: 2 | Pages: 33292-33301 | April 2026 | https://doi.org/10.48084/etasr.15817

Received: 28 October 2025 | Revised: 18 January 2026 | Accepted: 28 January 2026 | Online: 13 February 2026

Corresponding author: Rachmad Andri Atmoko

Abstract

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%).

Keywords:

federated learning, explainable AI, blockchain, SHAP, trust-based aggregation, adaptive federated learning, multi-model validation, medical AI

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References

H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, vol. 54, pp. 1273–1282, 2017.

P. Kairouz and H. B. McMahan, "Advances and Open Problems in Federated Learning," Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1–210, June 2021.

N. Rieke et al., "The Future of Digital Health with Federated Learning," npj Digital Medicine, vol. 3, no. 1, Sept. 2020, Art. no. 119.

Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated Machine Learning: Concept and Applications," ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, Mar. 2019.

C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, "A Survey on Federated Learning," Knowledge-Based Systems, vol. 216, Mar. 2021, Art. no. 106775.

Q. Li et al., "A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3347–3366, Apr. 2023.

L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, "Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges," IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1759–1799, 2021.

A. Imteaj, U. Thakker, S. Wang, J. Li, and M. H. Amini, "A Survey on Federated Learning for Resource-Constrained IoT Devices," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1–24, Jan. 2022.

V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, "A Survey on Security and Privacy of Federated Learning," Future Generation Computer Systems, vol. 115, pp. 619–640, Feb. 2021.

L. Lyu et al., "Privacy and Robustness in Federated Learning: Attacks and Defenses," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 8726–8746, July 2024.

M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, "Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications," IEEE Access, vol. 8, pp. 140699–140725, 2020.

H. Zhu, J. Xu, S. Liu, and Y. Jin, "Federated Learning on Non-IID Data: A Survey," Neurocomputing, vol. 465, pp. 371–390, Nov. 2021.

X. Ma, J. Zhu, Z. Lin, S. Chen, and Y. Qin, "A State-of-the-Art Survey on Solving Non-IID Data in Federated Learning," Future Generation Computer Systems, vol. 135, pp. 244–258, Oct. 2022.

K. Wei et al., "Federated Learning with Differential Privacy: Algorithms and Performance Analysis," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3454–3469, 2020.

S. Truex, L. Liu, K.-H. Chow, M. E. Gursoy, and W. Wei, "LDP-Fed: Federated Learning with Local Differential Privacy," in Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking, Heraklion, Greece, Apr. 2020, pp. 61–66.

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, "Federated Optimization in Heterogeneous Networks," in Proceedings of the 3rd Conference on Machine Learning and Systems, Austin, TX, USA, 2020.

S. A. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, "SCAFFOLD: Stochastic Controlled Averaging for Federated Learning," in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, pp. 5132–5143.

X. Li, M. Jiang, X. Zhang, M. Kamp, and Q. Dou, "FedBN: Federated Learning on Non-IID Features via Local Batch Normalization." arXiv, 2021.

J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, "Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization," in NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, Dec. 2020, pp. 7611–7623.

C. Fung, C. J. M. Yoon, and I. Beschastnikh, "Mitigating Sybils in Federated Learning Poisoning," arXiv, 2018.

P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent," in Advances in Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 118–128.

X. Cao, M. Fang, J. Liu, and N. Z. Gong, "FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping," in Proceedings 2021 Network and Distributed System Security Symposium, Virtual, 2021.

Z. Zhang, X. Cao, J. Jia, and N. Z. Gong, "FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients," in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, Aug. 2022, pp. 2545–2555.

J. Wen, Z. Zhang, Y. Lan, Z. Cui, J. Cai, and W. Zhang, "A Survey on Federated Learning: Challenges and Applications," International Journal of Machine Learning and Cybernetics, vol. 14, no. 2, pp. 513–535, Feb. 2023.

K. Pfeiffer, M. Rapp, R. Khalili, and J. Henkel, "Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey," ACM Computing Surveys, vol. 55, no. 14s, pp. 1–27, Dec. 2023.

M. T. Ribeiro, S. Singh, and C. Guestrin, "‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, Aug. 2016, pp. 1135–1144.

S. M. Lundberg and S. I. Lee, "A Unified Approach to Interpreting Model Predictions," in Advances in Neural Information Processing Systems, U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, R. Garnett, Eds. Red Hook, NY: Curran Associates, Inc, 2018.

A. Barredo Arrieta et al., "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82–115, June 2020.

R. Dwivedi et al., "Explainable AI (XAI): Core Ideas, Techniques, and Solutions," ACM Computing Surveys, vol. 55, no. 9, pp. 1–33, Sept. 2023.

I. C. Covert, S. Lundberg, and S. I. Lee, "Explaining by Removing: A Unified Framework for Model Explanation," Journal of Machine Learning Research, vol. 22, no. 2021, pp. 1–90, Sept. 2021.

A. Holzinger, A. Saranti, C. Molnar, P. Biecek, and W. Samek, "Explainable AI Methods - A Brief Overview," in xxAI - Beyond Explainable AI, vol. 13200, A. Holzinger, R. Goebel, R. Fong, T. Moon, K.-R. Müller, and W. Samek, Eds. Cham, Switzerland: Springer International Publishing, 2022, pp. 13–38.

P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, "Explainable AI: A Review of Machine Learning Interpretability Methods," Entropy, vol. 23, no. 1, Dec. 2020, Art. no. 18.

D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. Vincent Poor, "Federated Learning for Internet of Things: A Comprehensive Survey," IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1622–1658, 2021.

A. Z. Tan, H. Yu, L. Cui, and Q. Yang, "Towards Personalized Federated Learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 9587–9603, Dec. 2023.

T. Li, S. Hu, A. Beirami, and V. Smith, "Ditto: Fair and Robust Federated Learning Through Personalization," in Proceedings of the 38th International Conference on Machine Learning, Virtual, July 2021, pp. 6357–6368.

Y. Zhan, J. Zhang, Z. Hong, L. Wu, P. Li, and S. Guo, "A Survey of Incentive Mechanism Design for Federated Learning," IEEE Transactions on Emerging Topics in Computing, pp., 1035–1044, 2021.

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, "Federated Learning with Non-IID Data," 2018.

J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, "Federated Learning: Strategies for Improving Communication Efficiency." arXiv, 2016.

X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, "On the Convergence of FedAvg on Non-IID Data," in Proceedings of the International Conference on Learning Representations, Virtual, 2020.

L. Collins, H. Hassani, A. Mokhtari, and S. Shakkottai, "Exploiting Shared Representations for Personalized Federated Learning," in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, pp. 2089–2099.

M. Chen, N. Shlezinger, H. V. Poor, Y. C. Eldar, and S. Cui, "Communication-Efficient Federated Learning," Proceedings of the National Academy of Sciences, vol. 118, no. 17, Apr. 2021, Art. no. e2024789118.

W. Liu, L. Chen, Y. Chen, and W. Zhang, "Accelerating Federated Learning via Momentum Gradient Descent," IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 8, pp. 1754–1766, Aug. 2020.

D. Yin, Y. Chen, R. Kannan, and P. Bartlett, "Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates," in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, pp. 5650–5659.

M. Shayan, C. Fung, C. J. M. Yoon, and I. Beschastnikh, "Biscotti: A Blockchain System for Private and Secure Federated Learning," IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7, pp. 1513–1525, July 2021.

J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang, "Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory," IEEE Internet of Things Journal, vol. 6, no. 6, pp. 10700–10714, Dec. 2019.

H. Wang, Z. Kaplan, D. Niu, and B. Li, "Optimizing Federated Learning on Non-IID Data with Reinforcement Learning," in IEEE Conference on Computer Communications, Toronto, ON, Canada, July 2020, pp. 1698–1707.

A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138–52160, 2018.

T. Shen et al., "Federated Mutual Learning." arXiv, 2020.

H. Kim, J. Park, M. Bennis, and S.-L. Kim, "Blockchained On-Device Federated Learning," IEEE Communications Letters, vol. 24, no. 6, pp. 1279–1283, June 2020.

Y. Li, C. Chen, N. Liu, H. Huang, Z. Zheng, and Q. Yan, "A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus," IEEE Network, vol. 35, no. 1, pp. 234–241, Jan. 2021.

S. R. Pokhrel and J. Choi, "Federated Learning with Blockchain for Autonomous Vehicles: Analysis and Design Challenges," IEEE Transactions on Communications, vol. 68, no. 8, pp. 4734–4746, Aug. 2020.

Y. Qu, M. P. Uddin, C. Gan, Y. Xiang, L. Gao, and J. Yearwood, "Blockchain-Enabled Federated Learning: A Survey," ACM Computing Surveys, vol. 55, no. 4, pp. 1–35, Apr. 2023.

M. Ali, H. Karimipour, and M. Tariq, "Integration of Blockchain and Federated Learning for Internet of Things: Recent Advances and Future Challenges," Computers & Security, vol. 108, Sept. 2021, Art. no. 102355.

Y. Lu, X. Huang, K. Zhang, S. Maharjan, and Y. Zhang, "Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles," IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4298–4311, Apr. 2020.

D. C. Nguyen et al., "Federated Learning for Smart Healthcare: A Survey," ACM Computing Surveys, vol. 55, no. 3, pp. 1–37, Mar. 2023.

W. Wolberg, O. Mangasarian, N. Street, and W. Street, "Breast Cancer Wisconsin (Diagnostic)." UCI Machine Learning Repository, 1993, [Online]. Available: https://archive.ics.uci.edu/dataset/17.

J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang, "Federated Learning for Healthcare Informatics," Journal of Healthcare Informatics Research, vol. 5, no. 1, pp. 1–19, Mar. 2021.

R. S. Antunes, C. André Da Costa, A. Küderle, I. A. Yari, and B. Eskofier, "Federated Learning for Healthcare: Systematic Review and Architecture Proposal," ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 4, pp. 1–23, Aug. 2022.

I. Dayan et al., "Federated learning for predicting clinical outcomes in patients with COVID-19," Nature Medicine, vol. 27, no. 10, pp. 1735–1743, Oct. 2021.

M. J. Sheller et al., "Federated Learning in Medicine: Facilitating Multi-institutional Collaborations Without Sharing Patient Data," Scientific Reports, vol. 10, no. 1, July 2020, Art. no. 12598.

A. Qayyum, K. Ahmad, M. A. Ahsan, A. Al-Fuqaha, and J. Qadir, "Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge," IEEE Open Journal of the Computer Society, vol. 3, pp. 172–184, 2022.

Q. Wu, X. Chen, Z. Zhou, and J. Zhang, "FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring," IEEE Transactions on Mobile Computing, vol. 21, no. 8, pp. 2818–2832, Aug. 2022.

C. Xie, O. Koyejo, and G. Gupta, "Asynchronous Federated Optimization," in 12th Annual Workshop on Optimization for Machine Learning, Virtual, 2020.

M. Mohri, G. Sivek, and A. T. Suresh, "Agnostic Federated Learning," in 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019.