LEADER 03701nam  22006375  450 
001 9910865239003321
005 20240701141216.0
010   $a9783031575679$b(electronic bk.)
010   $z9783031575662
024 7 $a10.1007/978-3-031-57567-9
035   $a(MiAaPQ)EBC31356408
035   $a(Au-PeEL)EBL31356408
035   $a(CKB)32166331600041
035   $a(DE-He213)978-3-031-57567-9
035   $a(MiAaPQ)EBC31574284
035   $a(Au-PeEL)EBL31574284
035   $a(EXLCZ)9932166331600041
100   $a20240528d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDistributed Machine Learning and Computing $eTheory and Applications /$fedited by M. Hadi Amini
205   $a1st ed. 2024.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024.
215   $a1 online resource (163 pages)
225 1 $aBig and Integrated Artificial Intelligence,$x2662-4141 ;$v2
311 08$aPrint version: Amini, M. Hadi Distributed Machine Learning and Computing Cham : Springer International Publishing AG,c2024 9783031575662 
327   $aChapter 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 Arti?cial 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.
330   $aThis book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques. Specifies the value of efficient theoretical methods in dealing with large-scale decision-making problems; Provides an investigation of distributed machine learning and optimization algorithms for large-scale networks; Includes basics and mathematical foundations needed to analyze and address the interdependent complex networks.
410  0$aBig and Integrated Artificial Intelligence,$x2662-4141 ;$v2
606   $aTelecommunication
606   $aComputational intelligence
606   $aMachine learning
606   $aCooperating objects (Computer systems)
606   $aCommunications Engineering, Networks
606   $aComputational Intelligence
606   $aMachine Learning
606   $aCyber-Physical Systems
615  0$aTelecommunication.
615  0$aComputational intelligence.
615  0$aMachine learning.
615  0$aCooperating objects (Computer systems)
615 14$aCommunications Engineering, Networks.
615 24$aComputational Intelligence.
615 24$aMachine Learning.
615 24$aCyber-Physical Systems.
676   $a621,382
700   $aAmini$b M. Hadi$0871400
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910865239003321
996   $aDistributed Machine Learning and Computing$94169393
997   $aUNINA