Federated Learning for Envision Future Trajectory Smart Transport System for Climate Preservation and Smart Green Planet: Insights into Global Governance and SDG-9 (Industry, Innovation and Infrastructure)

Abstract

The integration of Federated Learning (FL) into the realm of smart transport systems offers a nuanced perspective that extends beyond technological innovation, exploring the intricate relationship between global governance and the pursuit of Sustainable Development Goal 9 (SDG-9), which emphasizes Industry, Innovation, and Infrastructure. FL's revolutionary decentralized machine learning paradigm harmonizes remarkably well with the foundational principles of SDG-9, unlocking a transformative pathway for the evolution of transportation networks while upholding the crucial aspects of data privacy and security. This integration stands as a dynamic testament to the convergence of cutting-edge technology and the overarching framework of global governance. FL's decentralized approach embodies the ideals of inclusivity and collaboration, echoing the ethos of SDG-9's call for resilient,
inclusive, and sustainable industrialization. By allowing individual nodes, vehicles, and infrastructural components to collaboratively improve machine learning models without sharing sensitive data, FL ensures equitable access to the benefits of technological advancements across various geographical and socioeconomic contexts. This intrinsic capability to pool insights from diverse sources paves the way for cross-border collaboration that transcends national boundaries and fosters a sense of collective responsibility toward infrastructure development, underscoring the essence of global governance. The fusion of Federated Learning with smart transport systems unveils a holistic perspective that intertwines technology, global governance, and the principles of Sustainable
Development Goal 9. This synergy propels the evolution of transportation infrastructure toward a sustainable, inclusive, and technologically empowered future. As, FL continues to reshape the landscape of intelligent transportation, it underscores the integral role that global governance plays in shaping the trajectory of sustainable industrialization, innovation, and infrastructure development, fostering a world where cutting-edge technology and equitable progress go hand in hand. The convergence of digital technology and transportation systems has given rise to the concept of smart transport, which aims to enhance efficiency, safety,
and sustainability in urban mobility. Federated learning, a decentralized machine learning paradigm, presents a promising avenue for
advancing smart transport systems by fostering collaboration among distributed entities while respecting data privacy. This research work delves into the realm of federated learning as a pivotal approach for developing a smart transport system aimed at climate preservation. As the world grapples with the challenges of climate change, achieving Sustainable Development Goal 9 (Industry, Innovation and Infrastructure) gains prominence. By employing federated learning, a decentralized machine learning technique, this study explores how global governance mechanisms can facilitate the development and implementation of a sustainable smart transport system. The study delves into the technical aspects of federated learning, its applicability to transportation, and the role it plays in advancing climate preservation and SDG-9.