Federated Learning-based Financial Risk Early Warning Model for Baijiu Enterprises

Abstract

As the development of economic globalization deepens, the early warning and management of corporate financial risks have become increasingly crucial. This paper addresses the characteristics of the baijiu (Chinese liquor) industry and proposes a federated learning-based financial risk early warning model to achieve a balance between data sharing and risk prediction among baijiu enterprises. Through federated learning, different enterprises can collaboratively train an early warning model while safeguarding data privacy, thereby enhancing prediction accuracy and comprehensiveness. This paper begins by reviewing federated learning and the field of financial risk early warning. It subsequently presents the model's design and implementation, and concludes by demonstrating the model's effectiveness and superiority in baijiu enterprise financial risk early warning through experiments.