Fed-Reputed: Reputation-Aware Client Selection in Hierarchical Federated Learning for Consumer Electronics

Keyphrases

• Hierarchical Federated Learning 100%
• Client Selection 100%
• Consumer Electronics 100%
• Reputation-aware 100%
• Imbalanced Classification 50%
• Stragglers 50%
• Malicious Clients 50%
• Markov Decision Process 25%
• Selection Framework 25%
• Classification Datasets 25%
• Selection Strategy 25%
• Model Accuracy 25%
• Bellman Equation 25%
• Health Monitoring 25%
• Federated Learning 25%
• Global Model 25%
• MNIST 25%
• Reputation-based 25%
• Learning Framework 25%
• Deep Q-learning 25%
• Fast Convergence 25%
• Biomedical Devices 25%
• Real-world Deployment 25%
• Restricted Connectivity 25%
• Model Integrity 25%
• Heterogeneous Environment 25%
• Device Capabilities 25%
• Low-resource 25%
• Consumer Devices 25%
• Herd Effect 25%
• Personalized Federated Learning 25%
• Historical Behavior 25%
• Biomedical Monitoring 25%
• Heterogeneous Federated Learning 25%
• Scalable Federated Learning 25%
• Collaborative Model Training 25%
• Mobile Health Diagnostics 25%

Computer Science

• Federated machine learning 100%
• Malicious Client 50%
• Learning Framework 25%
• Selection Method 25%
• Model Accuracy 25%
• Markov Decision Process 25%
• Critical Application 25%
• Fast Convergence 25%
• Consumer Device 25%
• Convergence Delay 25%
• Essential Feature 25%
• Collaboration 25%