Asynchronous Multi-Model Dynamic Federated Learning Over Wireless Networks: Theory, Modeling, and Optimization

Keyphrases

• Network Model 100%
• Model Dynamics 100%
• Network Theory 100%
• Multi-model 100%
• Network Optimization 100%
• Theory Modeling 100%
• Federated Learning 100%
• Federated Learning over Wireless Networks 100%
• Numerical Experiments 20%
• Energy Consumption 20%
• System Parameters 20%
• Non-convexity 20%
• Resource Allocation 20%
• Gradient Descent 20%
• Learning Performance 20%
• Successive Convex Approximation 20%
• Allocation Scheduling 20%
• Constraint Relaxation 20%
• Model Updating 20%
• Updating Model 20%
• Performance Efficiency 20%
• Mixed Integer Programming 20%
• Dynamic Data 20%
• Data Distribution 20%
• Efficiency Trade-offs 20%
• Local Gradient 20%
• Static Data 20%
• Distributed Machine Learning 20%
• Machine Learning Training 20%
• Wireless Environment 20%
• Joint Impact 20%
• Machine Learning Performance 20%
• Downstream Task 20%
• Asynchronous Model 20%
• Data Drift 20%
• Task Model 20%
• Training Variables 20%
• Asynchronous Scheduling 20%
• On-device Training 20%
• Convergence Guarantee 20%
• Scheduling Decision 20%
• Energy Machines 20%
• Efficient Trade 20%
• Device Scheduling 20%
• Machine Learning Model Training 20%
• Single-task Model 20%

Computer Science

• Wireless Network 100%
• Federated machine learning 100%
• Learning Performance 33%
• Machine Learning 33%
• Learning System 33%
• Approximation (Algorithm) 16%
• Energy Consumption 16%
• Integer Program 16%
• Gradient Descent 16%
• System Parameter 16%
• Distributed Machine Learning 16%
• Data Distribution 16%
• Resource Allocation 16%
• Machine Learning Model 16%