Foundations of Federated Learning

Part I: Foundations of Federated Learning.- Chapter 1: Introduction to Federated Learning.- Chapter 2: Fundamentals and Definitions of Federated Learning.- Chapter 3: Mathematical Formulation of Federated Learning.- Part II: Federated Learning Architectures and Training Protocols.- Chapter 4: Federated Learning System Architectures.-Chapter 5: Decentralized and Peer-to-Peer Federated Learning.- Chapter 6: Federated Learning Training Process.- Part III: Data Heterogeneity and Non-IID Learning.- Chapter 7: Data Heterogeneity and Non-IID Challenges.- Chapter 8: Techniques for Handling Non-IID Data.- Part IV: Federated Optimization Algorithms.- Chapter 9: Foundations of Federated Optimization Algorithms.- Chapter 10: Federated Averaging.- Chapter 11: Advanced Federated Optimization
Methods.- Part V: Personalized, Heterogeneous, and Hybrid Federated Learning.- Chapter 12: Personalized Federated Learning.- Chapter 13: Heterogeneous Federated Learning.- Chapter 14: Hybrid Federated Learning.- Part VI: Privacy, Security, Governance, and Applications.- Chapter 15: Privacy-Preserving Techniques in Federated Learning.- Chapter 16: Security Threats and Robust Federated Learning.- Chapter 17: Governance, Ethics, and Incentive Mechanisms.- Chapter 18: Applications, Emerging Trends and Tools.

• Erscheinungsdatum: 14.10.2026
• Autor/Autorin: Partha Pratim Ray
• Reihe: Mathematics and Statistics (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 16.1 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 780 Seiten
• ISBN: 9783032340849
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.