Buch, Englisch, 408 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 639 g
19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part III
Buch, Englisch, 408 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 639 g
ISBN: 978-3-031-54530-6
Verlag: Springer Nature Switzerland
The 72 full papers presented in these proceedings were carefully reviewed and selected from 176 submissions. The papers are organized in the following topical sections:
Volume I : Collaborative Computing, Edge Computing & Collaborative working, Blockchain applications, Code Search and Completion, Edge Computing Scheduling and Offloading.
Volume II: Deep Learning and Application, Graph Computing, Security and Privacy Protection and Processing and Recognition.
Volume III: Onsite Session Day2, Federated learning and application, Collaborative working, Edge Computing and Prediction, Optimization and Applications.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
- Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur
Weitere Infos & Material
Onsite Session Day 2.- Multi-agent Reinforcement Learning based Collaborative Multi-task Scheduling for Vehicular Edge Computing.- A Novel Topology Metric for Indoor Point Cloud SLAM Based on Plane Detection Optimization.- On the Performance of Federated Learning Network.- Federated learning and application.- FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification.- CSA_FedVeh: Cluster-based Semi-Asynchronous Federated Learning framework for Internet of Vehicles.- Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method.- A Novel Deep Federated Learning-based and Profit-Driven Service Caching Method.- A Multi-Behavior Recommendation Algorithm Based on Personalized Federated Learning.- FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks.- Collaborative working.- Enhance broadcasting throughput by associating network coding with UAVs relays deployment in emergency communications.- Dynamic Target User Selection Model For Market Promotion with Multiple Stakeholders.- Collaborative Decision-making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution.- Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines.- Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming.- Edge Computing.- Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems.- Enhancing Session-based Recommendation with Multi-granularity User Interest-aware Graph Neural Networks.- Delay-constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways.- An Evolving Transformer Network based on Hybrid Dilated Convolution for Traffic Flow Prediction.- Prediction, Optimization and Applications.- DualDNSMiner: A Dual-stack Resolver Discovery Method Based on Alias Resolution.- DT-MUSA: Dual Transfer Driven Multi-Source Domain Adaptation for WEEE Reverse Logistics Return Prediction.- A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning.
