Artificial Intelligence for Edge Computing

Part I: Core Problems.- Chapter 1: Neural Network Models for Time Series Data.- Chapter 2: Self-Supervised Learning from Unlabeled IoT Data.- Chapter 3: On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models.- Chapter 4: Out of Distribution Detection.- Chapter 5: Model Compression for Edge Computing.- Part II: Distributed Problems.- Chapter 6: Communication Efficient Distributed Learning.- Chapter 7: Coreset-based Data Reduction for Machine Learning at the Edge.- Chapter 8: Lightweight Collaborative Perception at the Edge.- Chapter 9: Dynamic Placement of Services at the Edge.- Chapter 10: Joint Service Placement and Request Scheduling at the Edge.- Part III: Cross-cutting Thoughts.- Chapter 11: Criticality-based Data Segmentation and Resource Allocation in Machine Inference Pipelines.- Chapter 12: Model Operationalization at Edge Devices.