Collaborative Computing: Networking, Applications and Worksharing

21st EAI International Conference, CollaborateCom 2025, Shanghai, China, November 15–16, 2025, Proceedings, Part II

Häftad, Engelska, 2026

Del 681 i serien Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-04-08
Mått:155 x 235 x 34 mm
Vikt:943 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Antal sidor:618
Förlag:Springer Nature Switzerland AG
ISBN:9783032211705

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Innehållsförteckning

.- 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 GroupPermutation 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.