Collaborative Computing: Networking, Applications and Worksharing (eBook, PDF)

20th EAI International Conference, CollaborateCom 2024, Wuzhen, China, November 14-17, 2024, Proceedings, Part III
Redaktion: Gao, Honghao; Wang, Xinheng

• Format: PDF

Produktbeschreibung

The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14-17, 2024.

The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows:

Edge computing & Task scheduling

Deep Learning and application

Blockchain applications

Security and Privacy Protection

Representation learning & Collaborative working

Graph neural networks & Recommendation systems

Federated Learning and application

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 320
• Erscheinungstermin: 9. August 2025
• Englisch
• ISBN-13: 9783031932571
• Artikelnr.: 75153219

Inhaltsangabe

Graph neural networks & Recommendation systems.- Time-aware Recommendations with Motif-Enhanced Graph Learning.- Spatial-Temporal Graph Attention Networks Based on Novel Adjacency Matrix For Weather Forecasting.- Repository-Level Code Generation Method Enhanced by Context-Dependent Graph Retrieval.- DGSR: Dual-Graph Sequential Recommendation with Gated and Heterogeneous GNNs.- Disentanglement-enhanced User Representation via Domain-level Clusters for Cross-Domain Recommendation.- Adaptive Web API Recommendation via Matching Service Clusters and Mashup Requirement.- Multi-channel Heterogeneous Graph Transformer based Unsupervised Anomaly Detection Model for IoT Time Series.- CBR-FIF: A Novel Dynamic Graph Node Embedding Computation Framework.- KG-ASI: A Knowledge Graph Enhanced Model-based Retriever for Document Retrieval.- Federated Learning and application.- Free-rider Attack Based on Data-free Knowledge Distillation in Federated Learning.- Client-Oriented Energy Optimization in Clustered Federated Learning with Model Partition.- FedUDA: Towards a Novel Unfairness Distribution Attack against Federated Learning Models.- Mal-GAT: A Method to Enhance Malware Traffic Detection with Graph Attention Networks.- A Federated Learning Framework with Blockchain and Cache Pools for Unreliable Devices in a Cloud-Edge-End Environment.- Model Similarity based Clustering Federated Learning in Edge Computing.- A Privacy-Preserving Edge Caching Algorithm Based on Permissioned Blockchain and Federated Reinforcement Learning.