A Federated Learning Driven Sustainable Computing Framework for Global Development Applications

Abstract

The rapid expansion of data-driven technologies across global development sectors such as healthcare, agriculture, education, smart governance, and climate monitoring has introduced significant challenges related to data privacy, energy consumption, and equitable access to computational resources. Centralized machine learning paradigms often exacerbate these issues by requiring large-scale data aggregation, high communication overhead, and energy-intensive cloud infrastructures. To address these limitations, this paper proposes a Federated Learning–Driven Sustainable Computing Framework tailored for global development applications. The proposed framework enables decentralized model training across geographically distributed edge and fog nodes, allowing sensitive data to remain locally stored while collaboratively learning a global model. The architecture integrates energy-aware client selection, adaptive communication scheduling, and lightweight model aggregation to minimize computational overhead and carbon footprint. Sustainability is further enhanced through dynamic workload balancing and resource-adaptive learning, enabling the framework to operate efficiently in low-power and resource-constrained environments common in developing regions. The framework is designed to support heterogeneous devices, intermittent connectivity, and non-IID data distributions, ensuring robustness and scalability across diverse socio-economic contexts. Experimental evaluation across representative global development use cases demonstrates that the proposed framework reduces communication costs by up to 45%, lowers energy consumption by approximately 38%, and maintains competitive model accuracy compared to centralized learning approaches. Moreover, privacy preservation and data sovereignty are inherently supported, aligning with ethical AI and regulatory requirements. The proposed federated learning-driven sustainable computing framework offers a practical and scalable pathway toward environmentally responsible, privacy-preserving, and inclusive artificial intelligence, contributing directly to the United Nations Sustainable Development Goals (SDGs) through responsible digital innovation.