A Distributed Hierarchical Deep Computation Model for Federated Learning in Edge Computing

Keyphrases

• Edge Computing 100%
• Deep Computation Model 100%
• Federated Learning 100%
• Edge Nodes 80%
• Hierarchical Tensor 40%
• Performance Evaluation 20%
• Machine Learning Techniques 20%
• Low-dimensional Subspace 20%
• Deep Learning 20%
• Learning Model 20%
• Training Data 20%
• Energy Consumption Reduction 20%
• Data Distribution 20%
• Training Effectiveness 20%
• Data Privacy 20%
• Tensor Space 20%
• Novel Machine 20%
• Back Propagation Algorithm 20%
• Memory Requirements 20%
• Model Update 20%
• Bandwidth Utilization 20%
• Dimensional Parameters 20%
• Storage Requirement 20%
• Shared Learning 20%
• Network Bandwidth 20%
• Big Data Analytics 20%
• Edge Computing System 20%
• Local Data 20%
• High-dimensional Tensor 20%

Computer Science

• Edge Computing 100%
• Federated Learning 100%
• Dimensional Subspace 20%
• Energy Consumption 20%
• Deep Learning 20%
• Performance Evaluation 20%
• Machine Learning Technique 20%
• Data Distribution 20%
• Training Data 20%
• Data Privacy 20%
• Computing Environment 20%
• Memory Requirement 20%
• Backpropagation Algorithm 20%
• Big Data Analytics 20%
• Storage Requirement 20%
• Communication Bandwidth 20%