HSFL: Efficient and Privacy-Preserving Offloading for Split and Federated Learning in IoT Services

Keyphrases

• Privacy-preserving 100%
• Federated Learning 100%
• Split Learning 100%
• Internet of Things Services 100%
• Model-based Decomposition 100%
• Privacy Protection 66%
• Decomposition Method 33%
• Machine Learning Techniques 33%
• Optimization Problem 33%
• Internet of Things 33%
• Client-server 33%
• Simulation Platform 33%
• Large-scale Dynamics 33%
• Training Model 33%
• Privacy Leakage 33%
• Machine Learning Models 33%
• Large-scale Dataset 33%
• Distributed Machine Learning 33%
• Internet of Things Environment 33%
• Edge Server 33%
• Contextual Bandit Problem 33%

Computer Science

• Privacy Preserving 100%
• Federated Learning 100%
• Internet-Of-Things 100%
• Model Decomposition 100%
• Privacy Protection 66%
• Experimental Result 33%
• Optimization Problem 33%
• Testbed 33%
• Client Server 33%
• Efficient Algorithm 33%
• Privacy Leakage 33%
• Training Model 33%
• Distributed Machine Learning 33%
• Decomposition Method 33%
• Machine Learning 33%
• Edge Server 33%