Collaborative Computing: Networking, Applications and Worksharing
20th EAI International Conference, CollaborateCom 2024, Wuzhen, China, November 14?17, 2024, Proceedings, Part III
Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; 626;
- Publisher's listprice EUR 90.94
-
38 576 Ft (36 739 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 7 715 Ft off)
- Discounted price 30 861 Ft (29 391 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
38 576 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer
- Date of Publication 1 July 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031932564
- Binding Paperback
- No. of pages309 pages
- Size 235x155 mm
- Language English
- Illustrations 82 Illustrations, black & white 700
Categories
Long 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
MoreTable 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.
More
