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Dynamic Split Federated Learning for resource-constrained IoT systems
Mohamad Wazzeh
, Ahmad Hammoud
, Azzam Mourad
, Hadi Otrok
,
Chamseddine Talhi
,
Zbigniew Dziong
, Chang Dong Wang
, Mohsen Guizani
École de technologie supérieure
Software and Information Technology Engineering Department
IMAGIN - laboratory of Innovation and engineering systeMs for Automation and diGitalisatIoN
LASI - Computer systems architecture laboratory
LCSec - Cybersecurity Laboratory
SYNCHROMÉDIA - Telepresence Multimedia Communications Laboratory
Electrical Engineering Department
École de technologie supérieure
Lebanese American University
Mohamed Bin Zayed University of Artificial Intelligence
Khalifa University of Science and Technology
Sun Yat-Sen University
Research output
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Contribution to journal
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Journal Article
›
peer-review
Overview
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Keyphrases
Internet of Things Devices
100%
Internet of Things System
100%
Split Federated Learning
100%
Resource-constrained
100%
Genetic Algorithm
50%
Training Model
50%
Dynamic Nature
25%
Accuracy Improvement
25%
Machine Learning
25%
Dynamic Model
25%
Proposed Architecture
25%
Resource Utilization
25%
Promising Solutions
25%
Internet of Things
25%
Federated Learning
25%
Data Privacy
25%
Resource-constrained Devices
25%
Resource Availability
25%
Privacy Concerns
25%
Efficient Use of Resources
25%
User Privacy
25%
Image-based
25%
Parallelization
25%
Training Tasks
25%
Dynamically Adjust
25%
Learning Architecture
25%
Heterogeneous Internet of Things
25%
Split Learning
25%
Heterogeneous Resources
25%
Client Selection
25%
Longest Processing Time
25%
Time Resources
25%
Centralized Data
25%
Point Selection
25%
Resource Heterogeneity
25%
Device Heterogeneity
25%
Efficient Training
25%
Device Training
25%
Collaborative Model
25%
Training Abilities
25%
On-chip Learning
25%
Metadata Analysis
25%
Split Point
25%
Computer Science
Internet-Of-Things
100%
Federated machine learning
100%
Internet of Things Device
80%
Genetic Algorithm
40%
Machine Learning
20%
Dynamic Nature
20%
Learning System
20%
Baseline Method
20%
Resource Utilisation
20%
Data Privacy
20%
Constrained Device
20%
Privacy Concern
20%
User Privacy
20%
Processing Time
20%
Parallelism
20%
Heterogeneous Resource
20%
Resource Availability
20%
Machine Learning Model
20%
Individual Client
20%
Aggregate Model
20%