The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, like differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations. With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world'scenarios – such as in healthcare, finance, IoT, and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, the book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems. Key Features: ·         Provides a comprehensive guide on tools and techniques of federated learning. ·         Highlights many practical real-world examples. ·         Includes easy to understand explanations.