Streamlining Financial Large Language Models for On-Device Time Series Analytics through Knowledge Distillation and Quantized Inference Architectures
DOI:
https://doi.org/10.66280/cis.v1i1.255Keywords:
Financial Large Language Models, On-Device Analytics, Knowledge Distillation, Quantized Inference, Edge Computing, Time Series Analysis, Socio-Technical Infrastructure, Algorithmic GovernanceAbstract
The proliferation of high-frequency financial data and the increasing demand for real-time decision-making have catalyzed a shift toward edge-based analytical frameworks. Large Language Models (LLMs) have demonstrated an unprecedented capacity for synthesizing complex financial narratives with numerical time series, yet their substantial computational requirements typically necessitate centralized cloud-based execution. This reliance on remote infrastructure introduces significant challenges related to latency, data privacy, and systemic vulnerability. This research proposes a systemic architecture for streamlining financial LLMs specifically for on-device time series analytics. By integrating advanced knowledge distillation techniques with quantized inference architectures, we demonstrate how the reasoning capabilities of multi-billion parameter teacher models can be effectively compressed into compact student models suitable for deployment on mobile and edge devices. This paper provides a deep analysis of the architectural trade-offs between model precision and hardware efficiency, emphasizing the role of hardware-aware quantization and specialized kernel optimization. Beyond the technical implementation, the discussion extends to the socio-technical implications of decentralized financial AI, focusing on algorithmic governance, the environmental sustainability of edge-to-cloud lifecycles, and the policy frameworks required to ensure fairness and robustness in autonomous localized trading environments. By providing a conceptual and structural blueprint for on-device financial intelligence, this work contributes to a more resilient, private, and efficient framework for global economic analysis, ensuring that the next generation of financial modeling is both computationally accessible and systemically secure.
References
1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
2. Bommasani, R., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
4. Cartea, A., Jaimungal, S., & Penalva, J. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press.
5. Chen, L., & Zheng, Z. (2023). LLM-augmented financial analysis: Challenges and opportunities. Journal of Financial Data Science, 5(4), 12-28.
6. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
7. Dwork, C. (2008). Differential privacy: A survey of results. International Conference on Theory and Applications of Models of Computation, 1-19.
8. Ghoshal, B., & Tucker, A. (2022). Scalable inference for deep learning in finance. Quantitative Finance, 22(10), 1845-1860.
9. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
10. Goyal, N., et al. (2023). High-throughput inference for large language models: A systems perspective. ACM SIGOPS Operating Systems Review, 57(1), 45-56.
11. Han, S., Pool, J., Tran, J., & Dally, W. J. (2015). Learning both weights and connections for efficient neural networks. Advances in Neural Information Processing Systems, 28.
12. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
13. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., ... & Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
14. Krishnamoorthi, R. (2018). Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342.
15. Li, M., et al. (2014). Scaling distributed machine learning with the parameter server. 11th USENIX Symposium on Operating Systems Design and Implementation.
16. Lo, A. W. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
17. Liu, T. (2026). Leakage-Safe Benchmark Design for Market-Stress Early Warning: An Economically Credible Evaluation.
18. Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
19. Narayanan, D., Phanishayee, A., Shi, K., Chen, X., & Zaharia, M. (2019). PipeDream: Generalized pipeline parallelism for DNN training. Proceedings of the 27th ACM Symposium on Operating Systems Principles.
20. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
21. Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
22. Polino, A., Kim, R., & Scavuzzo, G. (2018). Model compression via distillation and quantization. International Conference on Learning Representations.
23. Rajbhandari, S., Rasley, J., Ruwase, O., & He, Y. (2020). ZeRO: Memory optimizations toward training trillion parameter models. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.
24. Shalf, J. (2020). The future of computing beyond Moore’s Law. Philosophical Transactions of the Royal Society A, 378(2166).
25. Shiller, R. J. (2019). Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press.
26. Stoica, I., et al. (2017). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation.
27. Vaswani, Ashish, et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
28. Wu, S., et al. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.
29. Zaharia, M., et al. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. 9th USENIX Symposium on Networked Systems Design and Implementation.
30. Zhang, K., et al. (2021). Causal discovery and forecasting in nonstationary environments. Journal of Machine Learning Research, 22, 1-36.
31. Zhou, Y., et al. (2022). Mixture-of-experts with exponential selection. arXiv preprint arXiv:2202.08906.
32. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computational Intelligence 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.
