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Federated Learning Applications in the Industrial Internet of Everything (IoE) [Kõva köide]

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  • Formaat: Hardback, 276 pages, kõrgus x laius: 235x155 mm, 45 Illustrations, color; 5 Illustrations, black and white; X, 276 p. 50 illus., 45 illus. in color., 1 Hardback
  • Sari: Studies in Systems, Decision and Control 611
  • Ilmumisaeg: 19-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031992695
  • ISBN-13: 9783031992698
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Federated Learning Applications in the Industrial Internet of Everything (IoE)Suurem pilt
  • Formaat: Hardback, 276 pages, kõrgus x laius: 235x155 mm, 45 Illustrations, color; 5 Illustrations, black and white; X, 276 p. 50 illus., 45 illus. in color., 1 Hardback
  • Sari: Studies in Systems, Decision and Control 611
  • Ilmumisaeg: 19-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031992695
  • ISBN-13: 9783031992698
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031992698.html

This book presents a comprehensive exploration of federated learning and its transformative potential across industries, focusing on privacy-preserving, decentralized AI solutions. It introduces novel frameworks and applications in healthcare, smart transportation, energy optimization, and Industry 4.0, emphasizing real-world use cases and addressing key challenges in privacy, scalability, and collaboration. By bridging theory and practice, the book provides actionable insights into implementing federated learning for dynamic, interconnected ecosystems like the Industrial Internet of Everything (IoE). Aimed at researchers, practitioners, and policymakers, it offers cutting-edge strategies to enhance efficiency, security, and innovation in diverse industrial domains.

.- Introduction to Federated Learning and its applications in the IoE.-
Techniques used in Federated Learning.- Federated Learning in the
Manufacturing Industry.- Federated Learning in the Transportation
Industry.- Federated Learning in the Healthcare Industry.- Challenges and
Opportunities in Federated Learning for the IoE.- Conclusion and future
research directions, etc.