Author(s) Name:  Yuanming Shi, Kai Yang, Zhanpeng Yang, Yong Zhou

About the Book:

   Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources.

Table of Contents

1. Primer on Artificial Intelligence
  1.1. Basics of Machine Learning
  1.2. Models of Deep Learning
  1.3. Model Training and Inference
2. Overview of Edge AI Systems
  2.1. Motivations and Applications
  2.2. Levels of Edge Intelligence
  2.3. Edge Inference Process
  2.4. Edge Training Process
3. Model Compression for On-Device Inference
  3.1. Problem Formulation
  3.2. Inexact Proximal Iteratively Reweighted Algorithm
4. Wireless MapReduce for Device Distributed Inference
  4.1. System Model
  4.2. Interference Alignment for Data Shuffling
  4.3. Difference-of-Convex Functions (DC) Programming for Low-Rank Optimization
  4.4. Summary
5. Wireless Cooperative Transmission for Edge Inference
  5.1. System Model
  5.2. Learning based Robust Optimization Approximation for Joint Chance Constraints
  5.3. Reweighted Power Minimization for Quadratic Constrained Group Sparse Beamforming
  5.4. Summary
6. Over-the-Air Computation for Federated Learning
  6.1. System Model
  6.2. Sparse and Low-Rank Optimization for Federated Learning
  6.3. Difference-of-Convex Functions (DC) Representations
  6.4. Summary
7. Blind Over-the-Air Computation for Federated Learning
  7.1. Problem Formulation
  7.2. Main Approach
  7.3. Summary
8. Reconfigurable Intelligent Surface Aided Federated Learning System
  8.1. System Model
  8.2. Alternating Low-Rank Optimization for Model Aggregation
  8.3. Summary
9. Communication-Efficient Algorithms for Edge AI
  9.1. Communication-Efficient Zeroth-Order Methods
  9.2. Communication-Efficient First-Order Methods
  9.3. Communication-Efficient Second-order Methods
  9.4. Communication-Efficient Federated Optimization
10. Future Research Directions
  10.1. Edge AI Hardware Design
  10.2. Edge AI Software Platforms
  10.3. Edge AI as a Service
  10.4. Security and Privacy Issues

ISBN:  9780128238172

Publisher:  Elsevier

Year of Publication:  2021

Book Link:  Home Page Url