Federated Learning: Unlocking the Power of Collaborative IntelligenceКНИГИ » ПРОГРАММИНГ
Название: Federated Learning: Unlocking the Power of Collaborative Intelligence Автор: M. Irfan Uddin, Wali Khan Mashwani Издательство: CRC Press Серия: Chapman & Hall/CRC Artificial Intelligence and Robotics Series Год: 2025 Страниц: 194 Язык: английский Формат: pdf (true) Размер: 15.6 MB
Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of Federated Learning. This book delves into Federated Learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.
The book begins with a survey of the fundamentals of Federated Learning (FI) 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.
Federated Learning has revolutionized the area of Machine Learning as Federated Learning offers decentralized privacy-preserving techniques for group model training without transferring the raw data. Federated Learning allows the use of multiple devices, and organizations prefer it as a client as it allows the use of local data to train a global model. This is compared to traditional centralized Machine Learning which collects data in one location. This decentralized method preserves the fundamental principle of data protection while enabling the development of innovative applications by tackling the important problem of data security and privacy.
Key Features · Provides a comprehensive guide on tools and techniques of Federated Learning. · Highlights many practical real-world examples. · Includes easy to understand explanations.
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