Collaborative Computing: Networking, Applications and Worksharing : 20th EAI International Conference, CollaborateCom 2024, Wuzhen, China, November 14–17, 2024, Proceedings, Part III / / edited by Honghao Gao, Xinheng Wang

• Autore: Gao Honghao
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
• Edizione: 1st ed. 2026.
• Descrizione fisica: 1 online resource (469 pages)
• Disciplina: 004.2
• Soggetto topico: Computer systems; Information storage and retrieval systems; Computer networks; Data protection; Software engineering; Computers, Special purpose; Computer System Implementation; Information Storage and Retrieval; Computer Communication Networks; Data and Information Security; Software Engineering; Special Purpose and Application-Based Systems
• Altri autori: WangXinheng
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Collaborative Computing: Networking, Applications and Worksharing
• ISBN: 3-031-93257-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911046555103321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, . 1867-822X ; ; 626