Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis

Keyphrases

• Algorithm Analysis 100%
• Analog Implementation 25%
• Analog Transmission 50%
• Central Server 25%
• Channel Fading 25%
• Channel Noise 25%
• Classification Task 25%
• Cloud Computing Applications 25%
• Co-channel Interference 25%
• Collaborative Training 25%
• Communication Efficiency 50%
• Convergence Analysis 100%
• Convergence Bounds 25%
• Convexity 25%
• Decentralized Stochastic Gradient Descent 50%
• Device-to-device (D2D) Communication 25%
• Device-to-device Networks 100%
• Digital Implementation 25%
• Digital Transmission 25%
• Dimensionality Reduction 25%
• Federated Learning 100%
• Image Classification 25%
• Internet of Things Devices 25%
• Learning Implementation 25%
• Network Algorithms 100%
• Network Convergence 100%
• Optimality Gap 25%
• Over-the-air Computation 25%
• Random Linear Coding 25%
• Ratio Level 25%
• Reduction Mechanism 25%
• Sharing Model 25%
• Signal-to-noise Ratio 25%
• Star Topology 25%
• Stochastic Gradient Algorithm 25%
• Transmission Scheme 25%
• Wireless 25%
• Wireless Devices 100%
• Wireless Systems 25%

Computer Science

• Classification Task 25%
• Cloud Computing 25%
• Data Center 25%
• Device-To-Device 100%
• Distributed Client 25%
• Federated Learning 100%
• Gradient Descent 50%
• Image Classification 25%
• Individual Client 25%
• Internet of Things Device 25%
• Mathematical Convergence 100%
• Network Algorithm 100%
• Noise-to-Signal Ratio 25%
• Over-The-Air Computation 25%
• Random Linear Coding 25%
• Reduction Mechanism 25%
• Renewed Interest 25%
• Star Topology 25%
• Transmission Scheme 25%
• Wireless Device 100%
• Wireless System 25%