<p class="jove_content">We demonstrate a reproducible protocol for implementing FedRisk, an explainable federated learning framework. The procedure includes semantic data harmonization, privacy-preserving entity alignment, differential privacy calibration, local GBDT training, global Transformer fusion, and gradient-compressed asynchronous updates, enabling secure, scalable, and regulation-compliant financial risk prediction across multiple institutions.</p>

explainable-federated-learning-model-for-cross-institutional-digital

Explainable Federated Learning Model for Cross-Institutional Digital Financial Data Collaboration and Risk Control

The fragmentation of financial data across institutions and stringent privacy regulations severely hinder collaborative risk management, leading to ...

Institution - Nantong Institute of Technology

Articles Published in JoVE