Edge AI and Federated Learning: A Data Engineer’s Guide to Distributed AI

The book, Edge AI and Federated Learning: A Data Engineer’s Guide to Distributed AI, represents a pioneering contribution to the fields of Edge Computing, Federated Learning (FL), and Artificial Intelligence (AI) by offering a comprehensive, technical, and implementation-driven approach to designing distributed AI systems. This work stands at the forefront of AI engineering, bridging the gap between theoretical research and real-world deployment of privacy-preserving and decentralized AI solutions. Given the increasing challenges associated with centralized AI architectures, including data privacy concerns, scalability issues, and regulatory compliance, this book provides original insights and innovative methodologies that redefine how AI models are trained and deployed in real-world applications. It equips data engineers, AI researchers, and industry professionals with a practical framework to integrate Federated Learning and Edge AI into large-scale, production-ready AI systems.