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Collaborative Computing: Networking, Applications and Worksharing

Overview of attention for book
Collaborative Computing: Networking, Applications and Worksharing
Springer Nature Switzerland

Table of Contents

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    Book Overview
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    Chapter 1 Multi-agent Reinforcement Learning Based Collaborative Multi-task Scheduling for Vehicular Edge Computing
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    Chapter 2 A Novel Topology Metric for Indoor Point Cloud SLAM Based on Plane Detection Optimization
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    Chapter 3 On the Performance of Federated Learning Network
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    Chapter 4 FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification
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    Chapter 5 CSA_FedVeh: Cluster-Based Semi-asynchronous Federated Learning Framework for Internet of Vehicles
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    Chapter 6 Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method
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    Chapter 7 A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method
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    Chapter 8 A Multi-behavior Recommendation Algorithm Based on Personalized Federated Learning
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    Chapter 9 FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks
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    Chapter 10 Enhance Broadcasting Throughput by Associating Network Coding with UAVs Relays Deployment in Emergency Communications
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    Chapter 11 Dynamic Target User Selection Model for Market Promotion with Multiple Stakeholders
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    Chapter 12 Collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution
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    Chapter 13 Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines
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    Chapter 14 Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming
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    Chapter 15 Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems
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    Chapter 16 Enhancing Session-Based Recommendation with Multi-granularity User Interest-Aware Graph Neural Networks
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    Chapter 17 Delay-Constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways
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    Chapter 18 An Evolving Transformer Network Based on Hybrid Dilated Convolution for Traffic Flow Prediction
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    Chapter 19 DualDNSMiner: A Dual-Stack Resolver Discovery Method Based on Alias Resolution
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    Chapter 20 DT-MUSA: Dual Transfer Driven Multi-source Domain Adaptation for WEEE Reverse Logistics Return Prediction
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    Chapter 21 A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning
Attention for Chapter 9: FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)

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Chapter title
FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks
Chapter number 9
Book title
Collaborative Computing: Networking, Applications and Worksharing
Published in
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, January 2024
DOI 10.1007/978-3-031-54531-3_9
Book ISBNs
978-3-03-154530-6, 978-3-03-154531-3
Authors

Shkurti, Lamir, Selimi, Mennan, Besimi, Adrian

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 March 2024.
All research outputs
#15,924,781
of 25,992,468 outputs
Outputs from Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
#1
of 1 outputs
Outputs of similar age
#156,544
of 365,243 outputs
Outputs of similar age from Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.8. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 365,243 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them