Federated Learning-Based Framework: A New Paradigm Proposed for Supply Chain Risk Management

This paper proposes federated learning-based frameworks for supply chain risk management to address data-sharing constraints. To validate, centralized federated learning with horizontal data was applied for delivery delay prediction using datasets from two textile suppliers: supplier 1 has less data and is considered small, while supplier 2, with more data, represents a larger one. The prediction model is developed using an artificial neural network within the federated framework. The results show that federated learning benefits suppliers, especially the ones with limited data. Notably, federated learning outperforms centralized learning and local standalone learning. This highlights its potential to address privacy and facilitate collaboration.