Collaborative Computing: Networking, Applications and Worksharing 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part III

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.