Full 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
Contents
Edge Computing & Task Scheduling.- Latency Energy aware Heterogeneous Resource Allocation and Task Scheduling in Industrial Cloud Edge Computing.- Backpressure-based Federated Learning Model Scheduling in Edge Computing.- Minimizing the Age of Knowledge in Application-oriented Mobile Edge Computing System with DRL-based Scheduling.- Dependency-Aware Task Offloading in Dynamic Network Environment with D2D Collaboration.- Delay Minimization for Downlink PD-NOMA Transmission with Index Coding in Cache-Aided Wireless Networks.- Fast Adaptive Caching Algorithm for Mobile Edge Networks Based on Meta-Reinforcement Learning.- Delay- and Cost-Aware Dynamic Service Migration in Collaborative Satellite Computing.- Towards Efficient Scheduling in Large Clusters Leveraging the Small-World Network Model.- A Dynamic Prioritization Task Offloading Strategy with Delay Constraints.- Task Scheduling Strategy among Multiple Local Mobile Clouds in Pervasive Edge Computing.- A Task Scheduling Strategy Based on Computing-Aware and Multi-Agent Collaborative Services in Pervasive Edge Computing.- Collaborative Vehicular Edge Cloud Computing Task Offloading Optimization Scheme Based on Deep Reinforcement Learning.- Deep Learning and Application.- NL-ATD: Spatio-Temporal Few-Shot Learning via Attention Transfer and Denoising Model.- A GCN-based DRL Approach for task migration and resource allocation in Heterogeneous Edge-Cloud Environments.- A Multi-Document Summarization Method for Customer Feedback Based on Large Language Models.- KaRe: Towards Flexible and Effective Machine Unlearning with Knowledge Alignment and Repair.- SWGCNN-BiLSTM: A Method for Detecting Unknown Attack Traffic within Imbalanced Samples.- Two-stage workflow scheduling based on deep reinforcement learning.- GRASP-SLAM: Gmapping-augmented DRL for Active SLAM using Policy gradient.- WiLDID:Low-Collaboration WiFi-Based Person Identification Via A Lightweight Deep Neural Network.- Dialogue Summarization by Integrating Structural Features and Improving Factual Consistency through Post-Editing.- TransAware: An Automatic Parallel Method for Deep Learning Model Training with Global Model Structure Awareness.- A Reliability Enhancement Scheme for Distributed Cloud Service Systems Based on Deep Reinforcement Learning.- Contrastive Learning-Based Finger-Vein Recognition Using Frequency-Mixup Augmentation and Time-Frequency Feature Fusion.- BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction.
