Cross-Market Financial Sentiment Tracking Model Based on Federated Learning and Multimodal Data
Keywords:
Federated learning, Financial sentiment, Multimodal sentiment analysis, Privacy protection, Cross-market predictionAbstract
To solve the problems of data silos and privacy protection among financial institutions, this paper builds a federated learning framework. Stock forum text, corporate news images, and trading behavior data are integrated into a multimodal sentiment analysis network. Each participating node shares only gradient information to ensure data security. The central server uses a meta-learning strategy to quickly adapt to new markets. The experiments cover over 1,000 active stocks from the stock exchanges in China, the United States and Europe. Results show that the model improves the F1 score of macro- and micro-level sentiment fluctuation prediction by 7.3%, and greatly reduces the computational load on a single node.
References
Xiao, Y., Tan, L., & Liu, J. (2025). Application of Machine Learning Model in Fraud Identification: A Comparative Study of CatBoost, XGBoost and LightGBM.
Gong, C., Zhang, X., Lin, Y., Lu, H., Su, P. C., & Zhang, J. (2025). Federated Learning for Heterogeneous Data Integration and Privacy Protection.
Zhang, B., Han, X., & Han, Y. (2025). Research on Multimodal Retrieval System of e-Commerce Platform Based on Pre-Training Model.
Li, J., Wu, S., & Wang, N. (2025). A CLIP-Based Uncertainty Modal Modeling (UMM) Framework for Pedestrian Re-Identification in Autonomous Driving.
Zhong, Z., Wang, B., & Qi, Z. (2025). A Financial Multimodal Sentiment Analysis Model Based on Federated Learning.
Tian, J., Lu, J., Wang, M., Li, H., & Xu, H. (2025). Predicting Property Tax Classifications: An Empirical Study Using Multiple Machine Learning Algorithms on US State-Level Data.
Wang, Y., Han, X., & Zhang, X. (2025). AI-Driven Market Segmentation and Multi-Behavioral Sequential Recommendation for Personalized E-Commerce Marketing.
Yuan, T., Zhang, X., & Chen, X. (2025). Machine Learning based Enterprise Financial Audit Framework and High Risk Identification. arXiv preprint arXiv:2507.06266.
Zhang, Z., Ding, J., Jiang, L., Dai, D., & Xia, G. (2024). Freepoint: Unsupervised point cloud instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 28254-28263).
Zhang, Z., Li, Y., Huang, H., Lin, M., & Yi, L. (2024, September). Freemotion: Mocap-free human motion synthesis with multimodal large language models. In European Conference on Computer Vision (pp. 403-421). Cham: Springer Nature Switzerland.
Zhang, F. (2025). Distributed Cloud Computing Infrastructure Management. International Journal of Internet and Distributed Systems, 7(3), 35-60.
Qiu, Y., & Wang, J. (2023, October). A machine learning approach to credit card customer segmentation for economic stability. In Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA (pp. 27-29).
Qiu, Y. (2024). Estimation of tail risk measures in finance: Approaches to extreme value mixture modeling. arXiv preprint arXiv:2407.05933.
Qiu, Y. (2024). Financial Deepening and Economic Growth in Select Emerging Markets with Currency Board Systems: Theory and Evidence. arXiv preprint arXiv:2406.00472.
Qiu, Y., & Wang, J. (2022). Credit Default Prediction Using Time Series-Based Machine Learning Models. In Artificial Intelligence and Applications.
Zhan, S. (2025). Machine Learning-Based Parking Occupancy Prediction Using OpenStreetMap Data.
Zhan, S., & Qiu, Y. (2025). Efficient Big Data Processing and Recommendation System Development with Apache Spark. benefits, 4, 6.
Zhan, S., Lin, Y., Zhu, J., & Yao, Y. (2025). Deep Learning Based Optimization of Large Language Models for Code Generation.
Gui, H., Fu, Y., Wang, B., & Lu, Y. (2025). Optimized Design of Medical Welded Structures for Life Enhancement.
Gui, H., Wang, B., Lu, Y., & Fu, Y. (2025). Computational Modeling-Based Estimation of Residual Stress and Fatigue Life of Medical Welded Structures.
Chen, F., Liang, H., Li, S., Yue, L., & Xu, P. (2025). Design of Domestic Chip Scheduling Architecture for Smart Grid Based on Edge Collaboration.
Chen, H., Ning, P., Li, J., & Mao, Y. (2025). Energy Consumption Analysis and Optimization of Speech Algorithms for Intelligent Terminals.
Zheng, J., & Makar, M. (2022). Causally motivated multi-shortcut identification and removal. Advances in Neural Information Processing Systems, 35, 12800-12812.
Yao, Y., Weng, J., He, C., Gong, C., & Xiao, P. (2024). AI-powered Strategies for Optimizing Waste Management in Smart Cities in Beijing.
Yang, M., Wu, J., Tong, L., & Shi, J. (2025). Design of Advertisement Creative Optimization and Performance Enhancement System Based on Multimodal Deep Learning.
Yang, M., Wang, Y., Shi, J., & Tong, L. (2025). Reinforcement Learning Based Multi-Stage Ad Sorting and Personalized Recommendation System Design.
Peng, H., Tian, D., Wang, T., & Han, L. (2025). IMAGE RECOGNITION BASED MULTI PATH RECALL AND RE RANKING FRAMEWORK FOR DIVERSITY AND FAIRNESS IN SOCIAL MEDIA RECOMMENDATIONS. Scientific Insights and Perspectives, 2(1), 11-20.
Lin, Y., Yao, Y., Zhu, J., & He, C. (2025, March). Application of Generative AI in Predictive Analysis of Urban Energy Distribution and Traffic Congestion in Smart Cities. In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE) (pp. 765-768). IEEE.
Chen, H., Li, J., Ma, X., & Mao, Y. (2025). Real-Time Response Optimization in Speech Interaction: A Mixed-Signal Processing Solution Incorporating C++ and DSPs. Available at SSRN 5343716.
Peng, H., Jin, X., Huang, Q., & Liu, S. (2025). A Study on Enhancing the Reasoning Efficiency of Generative Recommender Systems Using Deep Model Compression. Available at SSRN 5321642.
Chen, H., Ma, X., Mao, Y., & Ning, P. (2025). Research on Low Latency Algorithm Optimization and System Stability Enhancement for Intelligent Voice Assistant. Available at SSRN 5321721.
Liang, R., Feifan, F. N. U., Liang, Y., & Ye, Z. (2025). Emotion-Aware Interface Adaptation in Mobile Applications Based on Color Psychology and Multimodal User State Recognition. Frontiers in Artificial Intelligence Research, 2(1), 51-57.
Yang, M., Cao, Q., Tong, L., & Shi, J. (2025, April). Reinforcement learning-based optimization strategy for online advertising budget allocation. In 2025 4th International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID) (pp. 115-118). IEEE.
Peng, H., Ge, L., Zheng, X., & Wang, Y. (2025). Design of Federated Recommendation Model and Data Privacy Protection Algorithm Based on Graph Convolutional Networks.
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Copyright (c) 2025 Ling Wu, Alex R. Johnson, Emily D. Carter, Michael B. Anderson
This work is licensed under a Creative Commons Attribution 4.0 International License.
