Federated Learning-Based Framework: A New Paradigm Proposed for Supply Chain Risk Management
Year: 2025
Type: conference-paper
Abstract: 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411158758