I. Introduction  
A. Our scope  
B. Contributions and structure of the paper  

II. Ubiquitous participants  
A. Definition  
B. Architecture and intersection with AI  

III. Fundamentals of Artificial Intelligence  
A. Background  
B. Performance metrics  
C. Pervasive frameworks for AI  
D. Pervasive AI for IoT Applications  
E. Lessons learned  

IV. Related Surveys and Paper Novelty  

V. Pervasive Training  
A. Federated Learning  
B. Multi-agent reinforcement learning  
C. Active Learning (AL)  

VI. Pervasive Inference  
A. Profiling computation and communication models  
B. Resource management for distributed inference  
C. Use case: Distribution on moving robots  

VII. Privacy of Pervasive AI Systems  
A. Privacy for pervasive training  
B. Privacy for pervasive inference  
C. Lessons learned  

VIII. Future Directions and Open Challenges  
A. Deployment of Pervasive AI in emerging systems  
B. Efficient algorithms for pervasive inference  
C. Enhanced federated learning algorithms  
D. Communication-efficient multi-agent reinforcement learning  

IX. Conclusion