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Lam M. Nguyen, Trong Nghia Hoang, Pin-Yu Chen

Federated Learning

Name: Federated Learning
Author: Lam M. Nguyen

Theory and Practice

Paperback Engels 2024 9780443190377

131,80

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Samenvatting

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 features
emerging 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.

Specificaties

ISBN13:9780443190377

Taal:Engels

Bindwijze:Paperback

Uitgever:Elsevier Science

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Inhoudsopgave

PART I: Optimization Fundamentals for Secure Federated Learning 1. Gradient Descent-Type Methods 2. Considerations on the Theory of Training Models with Differential Privacy 3. Privacy Preserving Federated Learning: Algorithms and Guarantees 4. Assessing Vulnerabilities and Securing Federated Learning 5. Adversarial Robustness in Federated Learning 6. Evaluating Gradient Inversion Attacks and Defenses PART II: Emerging Topics 7. Personalized federated learning: theory and open problems 8. Fairness in Federated Learning 9. Meta Federated Learning 10. Graph-Aware Federated Learning 11. Vertical Asynchronous Federated Learning: Algorithms and theoretical guarantees 12. Hyperparameter Tuning for Federated Learning - Systems and Practices 13. Hyper-parameter Optimization for Federated Learning 14. Federated Sequential Decision-Making: Bayesian Optimization, Reinforcement Learning and Beyond 15. Data Valuation in Federated Learning PART III: Applications and Ethical Considerations 16. Incentives in Federated Learning 17. Introduction to Federated Quantum Machine Learning 18. Federated Quantum Natural Gradient Descent for Quantum Federated Learning 19. Mobile Computing Framework for Federated Learning 20. Federated Learning for Privacy-preserving Speech Recognition 21. Ethical Considerations and Legal Issues Relating to Federated Learning

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