Federated Learning: Theory and Practice

Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors. Presents the fundamentals and a survey of key developments in the field of federated learningProvides emerging, state-of-the art topics that build on fundamentalsContains industry applicationsGives an overview of visions of the future INDICE: PART I: Optimization Fundamentals for Secure Federated Learning1. Gradient Descent-Type Methods2. Considerations on the Theory of Training Models with Differential Privacy3. Privacy Preserving Federated Learning: Algorithms and Guarantees4. Assessing Vulnerabilities and Securing Federated Learning5. Adversarial Robustness in Federated Learning6. Evaluating Gradient Inversion Attacks and DefensesPART II: Emerging Topics7. Personalized federated learning: theory and open problems8. Fairness in Federated Learning9. Meta Federated Learning10. Graph-Aware Federated Learning11. Vertical Asynchronous Federated Learning: Algorithms and theoretical guarantees12. Hyperparameter Tuning for Federated Learning - Systems and Practices13. Hyper-parameter Optimization for Federated Learning14. Federated Sequential Decision-Making: Bayesian Optimization, Reinforcement Learning and Beyond15. Data Valuation in Federated LearningPART III: Applications and Ethical Considerations16. Incentives in Federated Learning17. Introduction to Federated Quantum Machine Learning18. Federated Quantum Natural Gradient Descent for Quantum Federated Learning19. Mobile Computing Framework for Federated Learning20. Federated Learning for Privacy-preserving Speech Recognition21. Ethical Considerations and Legal Issues Relating to Federated Learning

• ISBN: 978-0-443-19037-7
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 434
• Fecha Publicación: 15/02/2024
• Nº Volúmenes: 1
• Idioma: Inglés