Distributed machine learning and computing : theory and applications / M. Hadi Amini, editor.
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 interest...
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| Online Access: |
Full Text (via Springer) |
|---|---|
| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer Nature Switzerland AG,
[2024]
|
| Series: | Big and integrated artificial intelligence ;
volume 2 |
| Subjects: |
MARC
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| 245 | 0 | 0 | |a Distributed machine learning and computing : |b theory and applications / |c M. Hadi Amini, editor. |
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| 490 | 0 | |a Big and integrated artificial intelligence ; |v volume 2 | |
| 520 | |a 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. | ||
| 505 | 0 | |a Intro -- Preface -- Contents -- About the Editor -- 1 Distributed Machine Learning and Computing: An Overview -- References -- 2 Distributed Multi-agent Meta-Learning for Trajectory Design in Wireless Drone Networks -- 2.1 Introduction -- 2.1.1 Related Works -- 2.2 Preliminaries of RL -- 2.2.1 Single Agent RL -- 2.2.2 Independent Multi-agent RL -- 2.2.3 Collaborative Multi-agent RL -- 2.3 Representative Work -- 2.3.1 System Model -- 2.3.1.1 Communication Performance Analysis -- 2.3.1.2 Utility Function Model -- 2.3.1.3 Problem Formulation | |
| 505 | 8 | |a 2.3.2 Value Decomposition-Reinforcement Learning Algorithm with Meta Training -- 2.3.2.1 Value Decomposition-Based Reinforcement Learning Components -- 2.3.2.2 Value Decomposition -- 2.3.2.3 Value Decomposition-Based Reinforcement Learning Solution -- 2.3.2.4 Convergence and Complexity Analysis -- 2.3.3 Meta Training Procedure -- 2.3.4 Simulation Results -- 2.4 Conclusions -- References -- 3 Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study -- 3.1 Introduction and Motivation -- 3.2 System Model and Parameters | |
| 505 | 8 | |a 3.2.1 General Distributed Machine Learning -- 3.2.2 Transition to Wireless MEL -- 3.2.2.1 Relationship to Completion Time and Energy Consumption -- 3.2.3 Problem Formulation -- 3.3 Synchronous MEL with Only Time Constraints -- 3.3.1 Formulation -- 3.3.2 Solution -- 3.4 Synchronous MEL with Dual-Time and Energy Constraints -- 3.4.1 Formulation -- 3.4.2 Proposed Solution -- 3.5 Heterogeneous Simulation Setup and MEL Algorithm -- 3.5.1 Heterogeneity Analysis -- 3.5.2 Simulation Environment -- 3.5.3 MEL Algorithm -- 3.6 Results and Discussions | |
| 505 | 8 | |a 3.6.1 Impact of Time Constraints on Local Model Updates -- 3.6.1.1 Improvements in Validation Accuracy -- 3.6.2 Comparing FL versus PL -- 3.6.3 Comparison to Centralized Approaches -- 3.6.4 Complexity Analysis and Execution Time -- 3.6.5 Performance with Energy Constraints -- 3.7 Extension of IoMT/H-IoT to EEG Data -- 3.7.1 Mathematical Formulation for EEG data -- Appendix 1 -- Appendix 2 -- Appendix 3 -- References -- 4 A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Healthcare Systems -- 4.1 Introduction -- 4.1.1 Emergence of AI, ML, and FL | |
| 505 | 8 | |a 4.1.2 AI and ML in Healthcare Applications -- 4.1.3 Federated Learning in Healthcare Application -- 4.1.4 Organization -- 4.2 Machine Learning (ML) and Artificial Intelligence (AI) Healthcare -- 4.2.1 Machine Learning Algorithms in Healthcare -- 4.2.2 ANN and DL for Healthcare -- 4.3 Applications of Artificial Intelligence and Machine Learning Healthcare in Precision Medicine -- 4.3.1 Diseases Diagnosis and Outbreak Prediction -- 4.3.2 Drug Discovery and Trial -- 4.3.3 Robotic Surgery -- 4.3.4 Precision Medicine -- 4.3.5 Bioinformatics -- 4.4 Federated Learning in Healthcare | |
| 588 | |a Description based on online resource; title from digital title page (viewed on June 25, 2024). | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Distributed artificial intelligence. | |
| 700 | 1 | |a Amini, M. Hadi. |1 https://id.oclc.org/worldcat/entity/E39PCjHDJpCMtPrhfb8XXdxjBX | |
| 776 | 0 | 8 | |i Print version: |a Amini, M. Hadi |t Distributed Machine Learning and Computing |d Cham : Springer International Publishing AG,c2024 |z 9783031575662 |
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