Yingyi Shu

You are already at the latest version This study addresses the conflict between data privacy and modeling performance in financial technology by proposing a federated learning-based privacy-preserving framework. The research first analyzes the sensitivity of user transaction data and the issue of cross-institutional data silos, highlighting the limitations of traditional centralized modeling in terms of privacy and compliance.

Submitted: 17 February 2026 You are already at the latest version This study proposes a novel distributionally robust portfolio optimization framework based on Wasserstein generative modeling, aiming to address the challenges of distributional uncertainty, tail risk, and structural drift in financial markets. The model integrates Wasserstein distance-based robust optimization with generative adversarial learning to jointly enhance risk control and return stability.

Submitted: 24 December 2025 You are already at the latest version Time-series anomaly detection faces significant challenges when dealing with imbalanced data distributions, distribution shifts, and heterogeneous feature types. Traditional supervised methods struggle due to limited labeled anomaly samples, while unsupervised approaches often produce high false positive rates.