Federated Learning-Based Capacity Sharing Strategies with Privacy-Preserving Trust Assessment
Keywords:
federated learning, capacity sharing, privacy preservation, trust assessment, distributed systems, collaborative economy, governanceAbstract
The intensifying complexity of modern supply chains, cloud computing infrastructures, and decentralized manufacturing networks has elevated the strategic importance of capacity sharing, wherein organizations pool computational, logistical, or production resources to enhance aggregate efficiency and resilience. Simultaneously, the proliferation of sensitive operational data and proprietary models raises profound privacy concerns that inhibit transparent collaboration. This paper presents a comprehensive systems-level analysis of federated learning-based capacity sharing strategies augmented with privacy-preserving trust assessment mechanisms. We conceptualize a decentralized architecture where participating nodes collaboratively train predictive models for capacity demand, resource availability, and failure risk without exposing raw data. Trust assessment is embedded through multi-dimensional reputation scoring, differential privacy guarantees, and secure aggregation protocols, ensuring that contributions are evaluated without violating confidentiality. The paper examines structural trade-offs among model accuracy, communication overhead, privacy budgets, and trust granularity. Governance frameworks that align technical trust metrics with contractual service-level agreements and incentive structures are explored, alongside fairness considerations to prevent marginalization of smaller participants. Deployment challenges, including heterogeneity of edge devices, intermittent connectivity, and energy sustainability, are discussed in the context of federated capacity sharing platforms. We further analyze how regulatory landscapes such as GDPR and the EU Data Act intersect with federated architectures, shaping data sovereignty and auditability requirements. The paper concludes by identifying future research directions in cross-domain capacity sharing, dynamic coalition formation, and the integration of zero-knowledge proofs for verifiable trust. The findings underscore that federated learning, when coupled with a nuanced trust assessment layer, can transform capacity sharing from brittle bilateral agreements into resilient, adaptive, and privacy-respecting ecosystems.
References
1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273–1282). PMLR.
2. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
3. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308–318). ACM.
4. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175–1191). ACM.
5. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non-IID data. arXiv preprint arXiv:1806.00582.
6. Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019). DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 18(5), 2438–2455.
7. Kang, J., Xiong, Z., Niyato, D., Xie, S., & Zhang, J. (2019). Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet of Things Journal, 6(6), 10700–10714.
8. Xie, C., Koyejo, S., & Gupta, I. (2019). Asynchronous federated optimization. arXiv preprint arXiv:1903.03934.
9. Fallah, A., Mokhtari, A., & Ozdaglar, A. (2020). Personalized federated learning: A meta-learning approach. In Advances in Neural Information Processing Systems (pp. 3657–3668).
10. Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
11. Hu, X., & Caldentey, R. (2023). Trust and reciprocity in firms’ capacity sharing. Manufacturing & Service Operations Management, 25(4), 1436-1450.
12. Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557.
13. Yu, H., Yang, L. T., & Zhang, Q. (2020). A fair-contribution-based incentive mechanism for federated learning. In Proceedings of the 2020 IEEE International Conference on Communications (pp. 1–6). IEEE.
14. Biais, B., Bisiere, C., Bouvard, M., & Casamatta, C. (2019). The blockchain folk theorem. Review of Financial Studies, 32(5), 1662–1715.
15. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (pp. 3315–3323).
16. Veale, M., & Borgesius, F. Z. (2021). Demystifying the draft EU artificial intelligence act. Computer Law & Security Review, 42, 105575.
17. European Commission. (2022). Proposal for a regulation on harmonised rules on fair access to and use of data (Data Act). COM(2022) 68 final.
18. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
19. Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y. C., Yang, Q., Niyato, D., & Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 2031–2063.
20. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
21. Qiu, X., Parcollet, T., Fernandez-Marques, J., Lane, N. D., & Almeida, M. (2022). Towards sustainable federated learning. arXiv preprint arXiv:2201.09730.
22. Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. (2019). Analyzing federated learning through an adversarial lens. In Proceedings of the 36th International Conference on Machine Learning (pp. 634–643). PMLR.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
