Edge intelligence in the making : optimization, deep learning, and applications / Sen Lin [and four others].

1. Introduction to edge intelligence
1.1. Artificial intelligence
1.2. Edge computing
1.3. Edge intelligence

2. Edge intelligence via model training
2.1. Architectures
2.2. Key performance indicators
2.3. Enabling technologies
2.4. Summary

3. Edge intelligence via federated meta-learning
3.1. Introduction
3.2. Related work
3.3. Preliminaries on meta-learning
3.4. Federated meta-learning for achieving real-time edge intelligence
3.5. Performance analysis of FedML
3.6. Robust federated meta-learning (FedML)
3.7. Experiments
3.8. Summary

4. Edge-cloud collaborative learning via distributionally robust optimization
4.1. Introduction
4.2. Basic setting for collaborating learning toward edge intelligence
4.3. Collaborative learning based on edge-cloud synergy of distribution uncertainty sets
4.4. Collaborative learning based on knowledge transfer of conditional prior distribution
4.5. Summary

5. Hierarchical mobile-edge-cloud model training with hybrid parallelism
5.1. Introduction
5.2. Background and motivation
5.3. HierTrain framework
5.4. Problem statement of policy scheduling
5.5. Optimization of policy scheduling
5.6. Performance evaluation
5.7. Summary

6. Edge intelligence via model inference
6.1. Architectures
6.2. Key performance indicators
6.3. Enabling technologies
6.4. Summary

7. On-demand accelerating deep neural network inference via edge computing
7.1. Introduction
7.2. Background and motivation
7.3. Framework and design
7.4. Performance evaluation
7.5. Summary

8. Applications, marketplaces, and future directions of edge intelligence
8.1. Applications of edge intelligence
8.2. Marketplace of edge intelligence
8.3. Future directions on edge intelligence.