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Collaborative Computing: Networking, Applications and Worksharing

Overview of attention for book
Collaborative Computing: Networking, Applications and Worksharing
Springer Nature Switzerland

Table of Contents

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    Book Overview
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    Chapter 1 Multi-agent Reinforcement Learning Based Collaborative Multi-task Scheduling for Vehicular Edge Computing
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    Chapter 2 A Novel Topology Metric for Indoor Point Cloud SLAM Based on Plane Detection Optimization
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    Chapter 3 On the Performance of Federated Learning Network
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    Chapter 4 FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification
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    Chapter 5 CSA_FedVeh: Cluster-Based Semi-asynchronous Federated Learning Framework for Internet of Vehicles
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    Chapter 6 Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method
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    Chapter 7 A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method
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    Chapter 8 A Multi-behavior Recommendation Algorithm Based on Personalized Federated Learning
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    Chapter 9 FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks
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    Chapter 10 Enhance Broadcasting Throughput by Associating Network Coding with UAVs Relays Deployment in Emergency Communications
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    Chapter 11 Dynamic Target User Selection Model for Market Promotion with Multiple Stakeholders
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    Chapter 12 Collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution
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    Chapter 13 Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines
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    Chapter 14 Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming
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    Chapter 15 Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems
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    Chapter 16 Enhancing Session-Based Recommendation with Multi-granularity User Interest-Aware Graph Neural Networks
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    Chapter 17 Delay-Constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways
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    Chapter 18 An Evolving Transformer Network Based on Hybrid Dilated Convolution for Traffic Flow Prediction
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    Chapter 19 DualDNSMiner: A Dual-Stack Resolver Discovery Method Based on Alias Resolution
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    Chapter 20 DT-MUSA: Dual Transfer Driven Multi-source Domain Adaptation for WEEE Reverse Logistics Return Prediction
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    Chapter 21 A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning
Attention for Chapter 9: FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks
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