A Study on Intelligent Decision-Making and Risk Management for Cross-Market Bidding via Integrated Federated Learning and R Language Modeling
DOI:
https://doi.org/10.54097/sgg82209Keywords:
Federated Learning, Cross-Market Bidding, Risk Management, Multi-Agent Simulation, Privacy-Preserving ComputationAbstract
Cross-market bidding across energy, finance, and supply chain sectors faces significant challenges due to data privacy constraints and commercial confidentiality, which hinder effective collaborative decision-making and risk management. This work proposes an integrated FL-R framework that synergistically combines Federated Learning for privacy-preserving collaborative modeling with the R language for statistical analysis and reproducible research. Through simulated case studies in electricity markets and supply chain finance, this work demonstrates that the proposed approach achieves an average reduction in prediction error of approximately 18% and an improvement in risk-adjusted returns of about 12%. The framework provides decision-makers with an effective tool for enhancing forecasting accuracy and strengthening risk control capabilities in complex multi-market environments.
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[1] Kiran, M., & Sobh, N. (2023). Federated learning for privacy-preserving data analysis: A survey. ACM Computing Surveys, 56(4), 1–36.
[2] García, J., & Fernández, F. (2015). Safe reinforcement learning: A comprehensive survey. Journal of Machine Learning Research, 16(1), 1437–1480.
[3] Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.
[4] Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., et al. (2021). Advances and open challenges in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
[5] Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., & Yu, H. (2019). Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13(3), 1–207.
[6] Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., et al. (2019). mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software, 4(44), 1903.
[7] Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2021). Shiny: Web application framework for R (Version 1.7.1), Computer software.
[8] Xie, Y. (2015). Dynamic documents with R and knitr (2nd ed.). Chapman and Hall/CRC.
[9] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
[10] Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21–42.
[11] Chopra, S., & Meindl, P. (2016). Supply chain management: Strategy, planning, and operation (7th ed.). Pearson.
[12] McMahan, B., Moore, E., Ramage, D., Hampson, S., & 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. PMLR, 1273–1282.
[13] Wang, H., Kaplan, Z., Niu, D., & Li, B. (2020). Optimizing federated learning on non-IID data with reinforcement learning. In IEEE Infocom 2020, 1698–1707.
[14] Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., et al. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.
[15] Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Springer.
[16] Kuhn, M. (2008). Constructing predictive models in R with the caret package. Journal of Statistical Software, 28(5), 1–26.
[17] Luraschi, J., Kralj, T., & Koirala, A. (2022). The R ecosystem: A review of the R language and its packages for data science. Journal of Open Source Software, 7(75), 3456.
[18] Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown: The definitive guide. Chapman and Hall/CRC.
[19] Allaire, J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2022). Quarto: Scientific and technical publishing system (Version 1.2). Computer software.
[20] Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., et al. (2019). A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (pp. 1–11).
[21] Goldreich, O. (2009). Foundations of cryptography: Volume 2, basic applications. Cambridge University Press.
[22] Beal, J., & Pianykh, O. (2022). Differential privacy in practice: Review and current challenges. IEEE Access, 10, 12389–12409.
[23] Ushey, K., Allaire, J., & Tang, Y. (2023). reticulate: Interface to Python (Version 1.28). Computer software.
[24] Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.
[25] Gronauer, S., & Diepold, K. (2022). Multi-agent deep reinforcement learning: A survey. Artificial Intelligence Review, 55(2), 895–943.
[26] Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49–58.
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