Mobile Edge Artificial Intelligence

Mobile Edge Artificial Intelligence

Opportunities and Challenges

1st Edition - August 7, 2021

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  • Authors: Yuanming Shi, Kai Yang, Zhanpeng Yang, Yong Zhou
  • Paperback ISBN: 9780128238172
  • eBook ISBN: 9780128238356

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Description

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.

Key Features

  • Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission
  • Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface
  • Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning

Readership

Scientists and researchers, postgraduates, undergraduates, practitioners and professionals in electronic engineering and computer science

Table of Contents

  • I. Introduction and Overview
    1. Primer on Artificial Intelligence
    1.1. Basics of Machine Learning
    1.1.1. Supervised Learning
    1.1.2. Unsupervised Learning
    1.1.3. Reinforcement Learning
    1.2. Models of Deep Learning
    1.2.1. Convolution Neural Network
    1.2.2. Recurrent Neural Network
    1.2.3. Graph Neural Network
    1.2.4. Generative Adversarial Network
    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
    2.3.1. Architectures
    2.3.2. Performance Indicators
    2.4. Edge Training Process
    2.4.1. Architectures
    2.4.2. Performance Indicators

    II. Edge Inference
    3. Model Compression for On-Device Inference
    3.1. Problem Formulation
    3.1.1. Layer-wise Pruning of Network
    3.1.2. Nonconvex Pruning Approach
    3.2. Inexact Proximal Iteratively Reweighted Algorithm
    3.2.1. Construction of Convex Surrogate Functions
    3.2.2. A Novel Termination Criterion
    3.2.3. Implementation of iPIR Based on ADMM
    3.2.4. Simulation Results
    3.3. Summary

    4. Wireless MapReduce for Device Distributed Inference
    4.1. System Model
    4.1.1. Computation Model
    4.1.2. Communication Model
    4.1.3. Achievable Data Rates and DoF
    4.2. Interference Alignment for Data Shuffling
    4.2.1. Interference Alignment Conditions
    4.2.2. Low-Rank Optimization Approach
    4.2.3. Problem Analysis
    4.3. Difference-of-Convex Functions (DC) Programming for Low-Rank Optimization
    4.3.1. Principles of DC Approach
    4.3.2. A Novel DC Representation for Rank Function
    4.3.3. Efficient DC Algorithm
    4.3.4. Simulation Results
    4.4. Summary

    5. Wireless Cooperative Transmission for Edge Inference
    5.1. System Model
    5.1.1. Wireless Communication Model
    5.1.2. Power Consumption Model
    5.1.3. Channel Uncertainty Model
    5.1.4. Problem Formulation
    5.2. Learning based Robust Optimization Approximation for Joint Chance Constraints
    5.2.1. Approximating Joint Chance Constraints via Robust Optimization
    5.2.2. Learning the High Probability Region from Data Samples
    5.2.3. Tractable Reformulations for Robust Optimization Problem
    5.2.4. Cost-Effective Sampling Strategy
    5.3. Reweighted Power Minimization for Quadratic Constrained Group Sparse Beamforming
    5.3.1. Matrix Lifting for Nonconvex Quadratic Constraints
    5.3.2. DC Representations for Rank-One Constraint
    5.3.3. Reweighted Algorithm for Inducing Group Sparsity
    5.3.4. Proposed Reweighted Power Minimization Approach
    5.3.5. Simulation Results
    5.4. Summary

    III. Edge Training
    6. Over-the-Air Computation for Federated Learning
    6.1. System Model
    6.1.1. On-Device Distributed Federated Learning
    6.1.2. Over-the-Air Computation for Model Aggregation
    6.1.3. Problem Formulation
    6.2. Sparse and Low-Rank Optimization for Federated Learning
    6.2.1. Sparse and Low-Rank Optimization
    6.2.2. Problem Analysis
    6.3. Difference-of-Convex Functions (DC) Representations
    6.3.1. DC Representation for Sparse Function
    6.3.2. DC Representation for Low-Rank Constraint
    6.3.3. A Unified DC Representation Framework
    6.3.4. DC Algorithms for Sparse and Low-Rank Optimization
    6.3.5. Simulation Results
    6.4. Summary

    7. Blind Over-the-Air Computation for Federated Learning
    7.1. Problem Formulation
    7.1.1. Blind Over-the-Air Computation
    7.1.2. Multi-Dimensional Nonconvex Estimation
    7.2. Main Approach
    7.2.1. Randomly Initialized Wirtinger Flow Algorithm
    7.2.2. Theoretical Analysis
    7.2.3. Simulation Results
    7.3. Summary

    8. Reconfigurable Intelligent Surface Aided Federated Learning System
    8.1. System Model
    8.1.1. Reconfigurable Intelligent Surface (RIS)-Aided Federated Learning System
    8.1.2. Problem Formulation
    8.2. Alternating Low-Rank Optimization for Model Aggregation
    8.2.1. A Two-Stage Framework
    8.2.2. Alternating Low-Rank Optimization
    8.2.3. Difference-of-Convex Functions Programming Algorithm
    8.2.4.Simulation Results
    8.3. Summary

    IV. Future Directions
    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

Product details

  • No. of pages: 206
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: August 7, 2021
  • Imprint: Academic Press
  • Paperback ISBN: 9780128238172
  • eBook ISBN: 9780128238356

About the Authors

Yuanming Shi

Yuanming Shi
Dr. Yuanming Shi is a tenured Associate Professor at the School of Information Science and Technology in ShanghaiTech University, China. He is an editor of IEEE Transactions on Wireless Communications and IEEE Journal on Selected Areas in Communications. His research areas include optimization, statistics, machine learning, signal processing, and their applications to 6G, IoT, and AI.

Affiliations and Expertise

Tsinghua University, Beijing, China

Kai Yang

Kai Yang
Mr. Kai Yang is currently at JD.com, Inc., China. His research interests include big data processing, mobile edge/fog computing, mobile edge artificial intelligence and dense communication networking. He has developed a wireless distributed computing framework for edge inference, and an over-the-air computation approach for edge federated machine learning.

Affiliations and Expertise

Dalian University of Technology, China

Zhanpeng Yang

Zhanpeng Yang
Mr. Zhanpeng Yang is shortly to join the School of Information Science and Technology, ShanghaiTech University. He mainly focuses on developing reconfigurable intelligence surface based 6G wireless technologies for mobile edge AI systems.

Affiliations and Expertise

Information Science and Technology, ShanghaiTech University

Yong Zhou

Yong Zhou
Dr. Yong Zhou is an Assistant Professor at the School of Information Science and Technology, ShanghaiTech University China. His research areas include optimization, machine learning, analysis, and resource allocation, with applications to beyond 5G, IoT, and edge networks.

Affiliations and Expertise

Shandong University, Jinan, China

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