Distributed Machine Learning and Computing : Theory and Applications / / edited by M. Hadi Amini

• Record Nr.: UNINA9910865239003321
• Autore: Amini M. Hadi
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024
• ISBN: 9783031575679; 9783031575662
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (163 pages)
• Collana: Big and Integrated Artificial Intelligence, , 2662-4141 ; ; 2
• Disciplina: 621,382
• Soggetti: Telecommunication; Computational intelligence; Machine learning; Cooperating objects (Computer systems); Communications Engineering, Networks; Computational Intelligence; Machine Learning; Cyber-Physical Systems
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1. Distributed Machine Learning and Computing: An Overview -- Chapter 2. Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks -- Chapter 3. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study -- Chapter 4. A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Health-care Systems -- Chapter 5. Vertical Federated Learning: Principles, Applications, and Future Frontiers -- Chapter 6. Decentralization of Energy Systems with Blockchain: Bridging Top-down and Bottom-up Management of the Electricity Grid.-Chapter 7. Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization.
• Sommario/riassunto: This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and ; engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques. Specifies the value of efficient theoretical methods in dealing with large-scale decision-making problems; Provides an investigation of distributed machine learning and optimization algorithms for large-scale networks; Includes basics and mathematical foundations needed to analyze and address the interdependent complex networks.