Leveraging Green Federated Learning for Sustainable Supply Chain Optimization in Developing Economies

A PHD research having the following content-Introduction-Literature and research framework(include hypotheses development)-Research model and method (a diagram illustrating the relationships between constructs)-data analysis and results-discussion and conclusionBelow are some instructions and data to facilitate this work.

Introduction/Background: The integration of Artificial Intelligence (AI) into supply chain management has revolutionized operational efficiencies and decision-making processes. However, the environmental implications of AI, especially concerning energy consumption and carbon emissions, have raised sustainability concerns. Federated Learning (FL), a decentralized AI approach, offers a potential solution by enabling model training across multiple devices without centralized data collection, thereby preserving data privacy and reducing transmission energy costs. Recent studies have introduced the concept of “Green Federated Learning,” focusing on minimizing the carbon footprint of AI models ​. Despite these advancements, the application of Green FL in optimizing supply chains, particularly in developing economies, remains underexplored.​

Research Problem and Gap: While Green Federated Learning presents a sustainable approach to AI model training, its practical application in real-world supply chain scenarios, especially within developing countries, lacks empirical research. The unique challenges of these economies—such as limited infrastructure, diverse energy sources, and data privacy concerns—necessitate tailored solutions. There is a critical need to investigate how Green FL can be effectively implemented to optimize supply chain operations while ensuring environmental sustainability in these contexts.​

Objectives:

1. To assess the feasibility of implementing Green Federated Learning in supply chain management within developing economies.
2. To develop and validate a Green FL-based model for optimizing supply chain operations, focusing on energy efficiency and carbon footprint reduction.
3. To identify the challenges and enablers of adopting Green FL in the supply chains of developing countries.​

Methodology:

• Literature Review: Conduct a comprehensive review of existing studies on Federated Learning, Green AI, and supply chain optimization to establish a theoretical foundation.
• Case Study Selection: Identify and select supply chain networks within developing economies that are amenable to AI integration.
• Model (Diagram): Design a Green FL framework tailored to the selected supply chains, incorporating energy-efficient algorithms and privacy-preserving mechanisms.
• Simulation and Testing: Implement the model in a simulated environment to evaluate performance metrics such as energy consumption, carbon emissions, and operational efficiency.
• Stakeholder Interviews: Engage with supply chain managers, IT professionals, and policymakers to gather insights on practical challenges and adoption readiness.​
• Design a research model diagram

Expected Outcomes:

• A validated Green Federated Learning model optimized for supply chain operations in developing economies.
• Quantitative data demonstrating reductions in energy usage and carbon emissions compared to traditional AI models.
• A set of best practices and guidelines for implementing Green FL in resource-constrained environments.
• Policy recommendations to support the adoption of sustainable AI technologies in supply chain management.​