Federated Quality Prediction and Explainability for Cross-Platform Large Model API Performance Monitoring
Keywords:
federated learning, quality prediction, large model APIs, explainable AI, SHAP, performance monitoring, cross-platform systems, AI governance, privacy preservationAbstract
The widespread deployment of large language models and other foundation models as API services has introduced unprecedented challenges in monitoring response quality across heterogeneous platforms. Traditional centralized monitoring approaches suffer from data locality constraints, privacy regulations, and the inability to capture platform-specific distributional shifts. This paper proposes a federated quality prediction framework that enables collaborative performance monitoring without centralizing raw response data. The framework integrates explainability techniques, particularly SHAP-based interpretability analysis, to provide actionable insights into the factors driving predicted quality scores across different deployment contexts. We examine the system architecture required for cross-platform federation, including communication protocols, aggregation strategies, and privacy-preserving mechanisms. The structural trade-offs between prediction accuracy, communication efficiency, and model transparency are analyzed in depth. A key contribution is the articulation of governance and policy implications for multi-stakeholder API ecosystems, where platform providers, model developers, and end-users have divergent incentives regarding quality accountability. Through case illustrations drawn from current large model API deployments, we demonstrate how federated explainability can support robust performance monitoring while respecting data sovereignty. The paper also addresses sustainability considerations, including the carbon footprint of distributed inference and the fairness implications of quality metrics that may systematically disadvantage smaller platforms. Forward-looking perspectives are offered on the integration of federated learning with continuous quality assurance for evolving large model APIs, emphasizing the need for standardized validation protocols and regulatory frameworks. This work aims to inform both researchers and practitioners designing next-generation monitoring infrastructures for foundational AI services.
References
1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
2. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (NeurIPS).
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
4. 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.
5. Zhang, Y., et al. (2020). A systematic review of quality prediction for cloud services. Journal of Cloud Computing, 9(1), 1-15.
6. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "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.
7. Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (NeurIPS).
8. Wang, S., et al. (2021). API performance monitoring in microservice architectures. IEEE Transactions on Services Computing, 14(3), 456-469.
9. 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.
10. Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674.
11. Molnar, C. (2020). Interpretable Machine Learning. Lulu.com.
12. Hard, A., Rao, K., Mathews, R., et al. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604.
13. Bhatt, U., et al. (2020). Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
14. Bonawitz, K., et al. (2019). Towards federated learning at scale: System design. In Proceedings of the 2nd SysML Conference.
15. Zhang, J., et al. (2022). Large model APIs: Challenges in reliability and cost. Communications of the ACM, 65(7), 78-87.
16. Luo, G., et al. (2023). A survey on model compression for large language models. arXiv preprint arXiv:2305.10625.
17. Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems (NeurIPS).
18. Gao, H., Zeng, W., Zhang, J., & Liang, Y. (2025, December). A large model API response quality prediction model based on least squares vector machine and SHAP interpretability analysis. In 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF) (pp. 438-442). IEEE.
19. Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2017). Federated multi-task learning. In Advances in Neural Information Processing Systems (NeurIPS).
20. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4), 211-407.
21. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
22. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Computer Science and Engineering Transactions
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.
