Collaborative Computing: Networking, Applications and Worksharing
Description
This two-volume set LNICST 680-681 constitutes the refereed proceedings of the 21st EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2025, held in Shanghai, China, during November 15-16, 2025.
The 58 full papers included in these volumes were carefully reviewed and selected from 207 submissions. They are categorized under the topical sections as follows:
Part I: Large Language Models & Recommendation systems; and Deep Learning and application.
Part II: Federated Learning & Collaborative working; Edge computing & Task scheduling; Security and Blockchain applications; and Anomaly Detection.
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Content
.- Federated Learning & Collaborative working.
.- A Data Fusion Processing Architecture for Multi-Node Network Cooperative Integrated Sensing and Communication.
.- Robustness Analysis of Multi-layer LEO Satellite Networks with Dynamic Heterogeneous Cascading Failure Model.
.- AC-KVS: Adaptive Centralized Key-Value Scheduler in Programmable Switch for Distributed Key-value Stores.
.- FedSTAR: A Federated Learning Framework for Reliable Trajectory Prediction under Spatiotemporal Heterogeneity.
.- A Hierarchical Model of Trusted Federation Based on Adaptive Mutual Learning.
.- A Service-Oriented Adaptive Hierarchical Incentive Solution for Federated Learning.
.- Correlation Aware Imbalanced Multimodal Fusion in IoT Environment.
.- Trust-Aware UAV-Vehicle Hierarchical Collaboration for Efficient Multimedia Big Data Collection.
.- PVA-FL: Practical Verifiable Aggregation for Privacy Preserving Federated Learning.
.- H2A-BPMN: A Hierarchical & Hybrid Agent Framework for Industrial BPMN Automated Modeling.
.- LADSG: Label-Anonymized Distillation and SimilarGradient Substitution for Label Privacy in Vertical Federated Learning.
.- Edge computing & Task scheduling.
.- CPRGO : Delay-Aware Task Scheduling Strategy for Edge-Cloud Continuum in Multi-Hop Network Environments.
.- Partial Pairwise Preference-Driven Task Allocation in Volunteer Crowdsourcing.
.- A deep matrix completion method for recovering edge collaborative sensing data in the Internet of Vehicles.
.- DCPS: A Novel Community-Interest-Aware Centralized Resource Scheduling Method for Cooperative MEC Caching.
.- Adaptive Multi-Objective Task Scheduling for K3s-Enabled UAV Swarms.
.- Adaptive Multi-Resource Orchestration for Latency Critical Service Function Chains in Mobile Edge Networks.
.- Joint Model Deployment and Task Offloading with Load Balancing for DNN Inference in Vehicular Edge Computing.
.- Security and Blockchain applications.
.- MVTest: Automated Metamorphic Testing of Multi-View Perception Systems.
.- Cyber-Attack Detection in Federated Learning: A Bidirectionally Secure and Verifiable Architecture.
.- GANBFL: A Reliable Incentive Mechanism for Federated Learning via GAN Based Evaluation and Blockchain Integration.
.- A Review of Hardware Accelerated Design and Optimization Techniques for Reconfigurable Cryptosystems.
.- CLCBA: A Secure and Efficient Identity Authentication Scheme for Cross-Chain Interoperability.
.- A Cross-Chain Key Agreement and Regulatory Governance Framework Based on Peninsula Group
Permutation Rational Functions.
.- Anomaly Detection.
.- SkyPatrol: Aerial Peer Perspective Vision based Anomalous UAV Recognition.
.- A Novel Dynamic Spatio-Temporal Collaborative Model for Multivariate Time Series Anomaly Detection.
.- Topology-enhanced Graph Attention Network for Anomaly Detection in IIoT Domain.
.- An Adversarial Detection and Defense Method based on Neural Discrete Representation.
.- MemGT: Memory-augmented Graph Transformer based Unsupervised Model for Collaborative Internet of Things Anomaly Detection.
.- Multi-scale Time-frequency Collaborative Feature Learning for Unsupervised Anomaly Detection in Fluctuating IoT Time Series.
