A Sequential Gradient-Based Multiple Access for Distributed Learning over Fading Channels

Keyphrases

• Multiple Access 100%
• Gradient-based 100%
• Fading Channels 100%
• Distributed Learning 100%
• Multiple Access Channel 66%
• Learning Problems 66%
• Network Edge 66%
• Loss Function 33%
• Convergence Rate 33%
• Large-scale Networks 33%
• Local Gradient 33%
• Transmission Signal 33%
• Distributed Nodes 33%
• Access Algorithm 33%
• Fading multiple Access Channel 33%
• Strongly Convex 33%
• Lipschitz Gradients 33%
• Convex Loss Function 33%
• Distorted Gradients 33%
• Local Loss 33%
• Large-scale Wireless Sensor Networks 33%
• Gradient Method 33%

Computer Science

• Multiple Access 100%
• Multiple Access Channel 100%
• Fading Channel 100%
• Distributed Learning 100%
• Learning Problem 66%
• Objective Function 33%
• Convergence Rate 33%
• Wireless Network 33%
• Gradient Descent 33%
• Transmitted Signal 33%