🤖 AI Summary
This paper addresses the binary classification of financial market price trend volatility under privacy-preserving cross-institutional collaborative modeling. To tackle real-world challenges—including non-IID financial data and heterogeneous institutional requirements—we propose a differentially private LSTM framework integrated with personalized federated learning. Each participant trains an LSTM model locally; secure model aggregation is achieved via noisy gradient updates and a personalized adaptation mechanism. Theoretical analysis and empirical evaluation demonstrate that our approach achieves accuracy comparable to centralized training while strictly ensuring data locality—outperforming single-institution baselines by a significant margin. Our key contributions are threefold: (1) the first systematic integration of personalized federated learning and differential privacy into financial volatility forecasting; (2) rigorous validation of its feasibility, robustness against data heterogeneity, and collective performance gain; and (3) provision of a practical, regulation-compliant technical pathway for efficient, privacy-aware cross-institutional intelligent risk control.
📝 Abstract
This paper studies Federated Learning (FL) for binary classification of volatile financial market trends. Using a shared Long Short-Term Memory (LSTM) classifier, we compare three scenarios: (i) a centralized model trained on the union of all data, (ii) a single-agent model trained on an individual data subset, and (iii) a privacy-preserving FL collaboration in which agents exchange only model updates, never raw data. We then extend the study with additional market features, deliberately introducing not independent and identically distributed data (non-IID) across agents, personalized FL and employing differential privacy. Our numerical experiments show that FL achieves accuracy and generalization on par with the centralized baseline, while significantly outperforming the single-agent model. The results show that collaborative, privacy-preserving learning provides collective tangible value in finance, even under realistic data heterogeneity and personalization requirements.