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Description
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
Table of Contents
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.
