Architecting Al at the Edge: Bridging Cloud and Devices presents a comprehensive and methodical exploration of how distributed intelligence is engineered across cloud, fog, and edge environments. It combines architectural depth with analytical clarity, making it an essential resource for technologists, researchers, and students navigating the rapidly evolving landscape of edge-centric Al systems.
The book unfolds progressively, from the evolution of distributed Al systems and workload partitioning to the design of adaptive, collaborative, and secure Al models. Readers are guided through the intricacies of hardware acceleration, communication protocols, data fabric design, and orchestration frameworks such as Kubernetes and KubeEdge. Real-world case studies, spanning healthcare IoT, predictive maintenance, and vehicular networks, demonstrate the tangible impact of these technologies.
Security and trust, often overlooked in technical architectures, receive dedicated focus through discussions on federated learning, homomorphic encryption, and blockchain integration. The concluding chapters open vistas into the future, TinyML, on-device generative models, and 6G-driven edge intelligence, revealing how innovation continues to reshape computational frontiers.
Balancing academic rigor with practical insight, this book serves as both a conceptual foundation and a technical guide. It empowers readers to design systems that are not only efficient and intelligent but also ethical, scalable, and resilient, qualities vital to the next generation of interconnected intelligence.