Integration of Federated Learning and Blockchain for Smart Cities

Stay ahead of the curve in urban innovation with this essential guide that provides a comprehensive roadmap for federated learning and blockchain to build secure, intelligent, and efficient smart city ecosystems.

As cities grow smarter, the demand for secure, decentralized, and privacy-preserving technologies is greater than ever. This book explores how federated learning and blockchain are transforming urban landscapes by enabling intelligent, secure, and efficient systems. By combining the power of decentralized machine learning with the transparency and security of blockchain, this book provides a roadmap for tackling challenges in urban mobility, energy management, public safety, and healthcare, delving into theoretical frameworks, architectural designs, security considerations, and real-world case studies to illustrate the impact of these technologies. This book serves as a comprehensive guide for researchers, industry professionals, and policymakers seeking to understand, implement, and innovate within smart city ecosystems.

Readers will find the volume:

• Explores the synergy between federated learning and blockchain, offering cutting-edge solutions for smart city challenges;
• Addresses critical issues of data privacy, decentralized AI, and secure digital infrastructure in urban environments;
• Features practical case studies on smart transportation, energy management, healthcare, and governance;
• Provides a forward-looking perspective on how emerging technologies will shape the cities of tomorrow.

Audience

Academics, researchers, industry professionals, and policymakers working in the fields of artificial intelligence, machine learning, blockchain, IoT, cybersecurity, smart city planning, and urban technology development.

Krishna Kant Singh, PhD is the Director at the Delhi Technical Campus, Greater Noida, India. He has authored 25 books and over 160 research papers in international journals. He is an associate editor of IEEE Transactions on Computational Social Systems, and Senior editor of IEEE Access. His research interests include machine vision, remote sensing, deep learning, and generative AI.

Akansha Singh, PhD is a professor in the School of Computer Science, Engineering, and Technology at Bennett University, Greater Noida, India, with over 18 years of teaching and research experience. She has published over 100 research papers and authored over 30 books in advanced areas of computer science. Her expertise spans image processing, deep learning, machine learning, remote sensing, and IoT, with a strong focus on AI-driven solutions for healthcare and environmental sustainability.

Mahesh T.R., PhD is the Program Head of the Department of Computer Science and Engineering in the School of Engineering and Technology, at Jain (Deemed-to-be University), Bengaluru, India. He has published over 180 research articles in international and edited several books. His research interests include image processing, machine learning, deep learning, artificial intelligence, IoT, and data science.