Cross-Market Financial Sentiment Tracking Model Based on Federated Learning and Multimodal Data - Wu, Ling; Johnson, Alex R.; Carter, Emily D.; Anderson, Michael B. (Journal of Theory and Practice in Engineering and Technology, 2025)
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.