1. Security and Privacy in Federated Learning (Digital Privacy and Security)
This groundbreaking work authored by Shui Yu and Lei Cui exemplifies the importance of safeguarding data in the rapidly evolving field of federated learning. Released on March 11, 2023, this book dissects the intricate balance between data utility and privacy, a vital topic in an age where data breaches are rampant. At a price of $152.55, it’s an investment for those serious about understanding and implementing privacy-preserving data practices in machine learning environments. The authors combine theoretical insights with practical applications, making this book essential for both researchers and practitioners. Explore the vital strategies for implementing privacy measures in federated learning and learn why it’s imperative for future data scientists.
2. Handbook of Trustworthy Federated Learning (Springer Optimization and Its Applications, 213)
My T. Thai, Hai N. Phan, and Bhavani Thuraisingham have authored this comprehensive handbook that sets the standard for trustworthy practices in federated learning. With its publication scheduled for September 4, 2024, at a price of $29.93, readers can look forward to valuable insights into enhancing transparency and security in collaborative machine learning frameworks. The book underscores various methodologies aimed at fostering trustworthiness in the implementation of federated systems. It serves as a pragmatic guide that extends beyond theory, showcasing real-world applications and case studies for a holistic understanding of the subject.
3. Federated Learning
Authored by Faiz ul Haque Zeya, Muhammad Faisal, and Generative Artificial Intelligence, this book laid a foundational framework for federated learning on July 24, 2023, priced affordably at $40. The text caters to both newcomers and seasoned data scientists, demystifying the concepts of federated learning through clear explanations and illustrative examples. By covering critical aspects, including algorithmic approaches and their practical implementations, the authors aim to equip readers with the knowledge needed to succeed in collaborative machine learning environments. Embrace the future of AI and learn how federated learning is revolutionizing the way organizations safeguard their data while leveraging insights.
4. Federated Learning for Wireless Networks
This essential text, written by Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, and Zhu Han, was published on January 1, 2022, with a price tag of $152.66. This book tackles the specific challenges and solutions of implementing federated learning within the context of wireless networks. As wireless technology continues to advance rapidly, understanding how to apply federated learning techniques is critical for researchers and engineers in the field. The authors provide a detailed analysis of algorithms and practical implementations, equipping readers with the tools to conduct cutting-edge research and develop robust applications in the sector.
5. Federated Learning for Future Intelligent Wireless Networks
Scheduled for release on December 27, 2023, authors Yao Sun, Chaoqun You, Gang Feng, and Lei Zhang introduce this vital book priced at $39.80. The text smoothly integrates the concepts of federated learning with the impending future of intelligent wireless networks. The authors share insights into how federated learning will shape the next generation of networked environments, from optimizing data communication to enhancing user experiences through personalized services. This book is a must-read for professionals eager to stay ahead of the curve in network technology.
6. Essential Federated Learning: AI at the Edge
Robert Johnson’s work, due for release on November 10, 2024, priced at $39.99, focuses on one of the most critical emerging trends in artificial intelligence: AI at the edge. This book elucidates how federated learning can enhance AI applications deployed directly on edge devices, ensuring data protection while maximizing computational efficiency. As the industry shifts towards decentralized AI systems, Johnson’s insights into federated methodologies and their applications will become indispensable for developers and researchers alike backed with real-world examples of their implementation.
7. Federated Learning: From Algorithms to System Implementation
On August 16, 2024, authors Liefeng Bo, Heng Huang, Songxiang Gu, and Yanqing Chen will unveil their comprehensive guide priced at $134. This book provides a detailed journey from the theoretical underpinnings of federated learning algorithms to their practical system implementations. This exhaustive approach makes it an invaluable resource for computer scientists and machine learning practitioners alike, as it integrates the latest research findings while offering practical guidelines for deploying federated learning in real-world scenarios. Readers will learn about the underlying principles and how they translate into tangible systems that can be applied across various fields.
8. Federated and Transfer Learning (Adaptation, Learning, and Optimization, 27)
Developed by Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, and Qiang Yang, this remarkable book, priced at $131.87 and published on October 1, 2022, delves into the synergy between federated learning and transfer learning. As machine learning continues to advance, the book highlights how combining these two approaches can lead to more robust and adaptable systems. The authors lay out strategies for integrating concepts from both areas, offering readers insights into building systems that learn efficiently across different domains and settings.
9. Distributed Machine Learning Patterns
Yuan Tang’s work set to launch on January 2, 2024, for $48.97 provides an in-depth look into machine learning patterns that optimize distributed systems. This book caters specifically to data scientists and engineers who wish to implement effective machine learning solutions in distributed environments. It covers essential techniques and code patterns that can help practitioners harness the power of distributed learning models, making it ideal for those looking to amplify their skills and impact in the ever-evolving field of AI.
10. Federated Learning Over Wireless Edge Networks (Wireless Networks)
In this insightful book authored by Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, and Chunyan Miao, published on September 28, 2022, at a price of $99, readers will explore the specific challenges of applying federated learning in wireless edge networks. As edge computing continues to gain traction, understanding how to optimize federated learning in these environments is key. This book provides theoretical foundations combined with practical insights, making it essential reading for engineers and researchers aiming to inform their approaches to data processing in edge scenarios.
