UID:
almahu_9949984401402882
Umfang:
1 online resource (208 pages)
ISBN:
9780128238356
,
0128238356
Inhalt:
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.--
Anmerkung:
Front Cover -- Mobile Edge Artificial Intelligence -- Copyright -- Contents -- List of figures -- Biography -- Yuanming Shi -- Kai Yang -- Zhanpeng Yang -- Yong Zhou -- Preface -- Acknowledgments -- Part 1 Introduction and overview -- 1 Motivations and organization -- 1.1 Motivations -- 1.2 Organization -- References -- 2 Primer on artificial intelligence -- 2.1 Basics of machine learning -- 2.1.1 Supervised learning -- 2.1.1.1 Logistic regression -- 2.1.1.2 Support vector machine -- 2.1.1.3 Decision tree -- 2.1.1.4 k-Nearest neighbors method -- 2.1.1.5 Neural network -- 2.1.2 Unsupervised learning -- 2.1.2.1 k-Means algorithm -- 2.1.2.2 Principal component analysis -- 2.1.2.3 Autoencoder -- 2.1.3 Reinforcement learning -- 2.1.3.1 Q-learning -- 2.1.3.2 Policy gradient -- 2.2 Models of deep learning -- 2.2.1 Convolutional neural network -- 2.2.2 Recurrent neural network -- 2.2.3 Graph neural network -- 2.2.4 Generative adversarial network -- 2.3 Summary -- References -- 3 Convex optimization -- 3.1 First-order methods -- 3.1.1 Gradient method for unconstrained problems -- 3.1.2 Gradient method for constrained problems -- 3.1.3 Subgradient descent method -- 3.1.4 Mirror descent method -- 3.1.5 Proximal gradient method -- 3.1.6 Accelerated gradient method -- 3.1.7 Smoothing for nonsmooth optimization -- 3.1.8 Dual and primal-dual methods -- 3.1.9 Alternating direction method of multipliers -- 3.1.10 Stochastic gradient method -- 3.2 Second-order methods -- 3.2.1 Newton's method -- 3.2.2 Quasi-Newton method -- 3.2.3 Gauss-Newton method -- 3.2.4 Natural gradient method -- 3.3 Summary -- References -- 4 Mobile edge AI -- 4.1 Overview -- 4.2 Edge inference -- 4.2.1 On-device inference -- 4.2.2 Edge inference via computation offloading -- 4.2.2.1 Server-based edge inference -- 4.2.2.2 Device-edge joint inference -- 4.3 Edge training.
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4.3.1 Data partition-based edge training -- 4.3.1.1 Distributed mode -- 4.3.1.2 Decentralized mode -- 4.3.2 Model partition-based edge training -- 4.4 Coded computing -- 4.5 Summary -- References -- Part 2 Edge inference -- 5 Model compression for on-device inference -- 5.1 Background on model compression -- 5.2 Layerwise network pruning -- 5.2.1 Problem statement -- 5.2.2 Convex approach for sparse objective and constraints -- 5.3 Nonconvex network pruning method with log-sum approximation -- 5.3.1 Log-sum approximation for sparse optimization -- 5.3.2 Iteratively reweighed minimization for log-sum approximation -- 5.4 Simulation results -- 5.4.1 Handwritten digits classification -- 5.4.2 Image classification -- 5.4.3 Keyword spotting inference -- 5.5 Summary -- References -- 6 Coded computing for on-device cooperative inference -- 6.1 Background on MapReduce -- 6.2 A communication-efficient data shuffling scheme -- 6.2.1 Communication model -- 6.2.2 Achievable data rates and DoF -- 6.3 A low-rank optimization framework for communication-efficient data shuffling -- 6.3.1 Interference alignment conditions -- 6.3.2 Low-rank optimization approach -- 6.4 Numerical algorithms -- 6.4.1 Nuclear norm relaxation -- 6.4.2 Iteratively reweighted least squares -- 6.4.3 Difference-of-convex (DC) programming approach -- 6.4.4 Computationally efficient DC approach -- 6.5 Simulation results -- 6.5.1 Convergence behaviors -- 6.5.2 Achievable DoF over local storage size -- 6.5.3 Scalability -- 6.6 Summary -- References -- 7 Computation offloading for edge cooperative inference -- 7.1 Background -- 7.1.1 Computation offloading -- 7.1.2 Edge inference via computation offloading -- 7.2 Energy-efficient wireless cooperative transmission for edge inference -- 7.2.1 Communication model -- 7.2.2 Power consumption model -- 7.2.3 Channel uncertainty model.
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7.2.4 Problem formulation -- 7.3 Computationally tractable approximation for probabilistic QoS constraints -- 7.3.1 Analysis of probabilistic QoS constraints -- 7.3.2 Scenario generation approach -- 7.3.3 Stochastic programming approach -- 7.3.4 Statistical learning-based robust optimization approach -- 7.3.4.1 Robust optimization approximation for probabilistic QoS constraints -- 7.3.4.2 Statistical learning approach for the high-probability region -- 7.3.4.2.1 Shape learning -- 7.3.4.2.2 Size calibration -- 7.3.4.3 Problem reformulation for problem P7.1.RO -- 7.3.5 A cost-effective channel sampling strategy -- 7.4 Reweighted power minimization approach with DC regularization -- 7.4.1 Nonconvex quadratic constraints -- 7.4.2 Reweighted power minimization with DC regularization -- 7.5 Simulation results -- 7.5.1 Benefits of considering CSI uncertainty -- 7.5.2 Advantages of overcoming the overconservativeness -- 7.5.3 Total power consumption -- 7.6 Summary -- References -- Part 3 Edge training -- 8 Over-the-air computation for federated learning -- 8.1 Background of federated learning and over-the-air computation -- 8.1.1 Federated learning -- 8.1.2 Over-the-air computation -- 8.2 System model -- 8.3 Fast model aggregation via over-the-air computation -- 8.3.1 A simple single-antenna case -- 8.3.2 Over-the-air computation for model aggregation with a multiantenna BS -- 8.3.3 Problem formulation -- 8.4 Sparse and low-rank optimization framework -- 8.5 Numerical algorithms -- 8.5.1 Convex relaxation approach -- 8.5.2 Iteratively reweighted minimization approach -- 8.5.3 DC programming approach -- 8.5.3.1 DC representations -- 8.5.3.2 DC program framework -- 8.6 Simulation results -- 8.6.1 Number of selected devices under MSE requirement -- 8.6.2 Performance of training an SVM classifier -- 8.7 Summary -- References.
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9 Reconfigurable intelligent surface aided federated learning -- 9.1 Background on reconfigurable intelligent surface -- 9.2 RIS empowered on-device distributed federated learning -- 9.2.1 System model -- 9.2.2 Problem formulation -- 9.3 Sparse and low-rank optimization framework -- 9.3.1 Two-step framework for sparse objective function -- 9.3.2 Alternating low-rank optimization for nonconvex biquadratic constraints -- 9.3.3 DC program for rank-one constraints -- 9.4 Simulation results -- 9.4.1 Device selection -- 9.4.2 Performance of federated learning -- 9.5 Summary -- References -- 10 Blind over-the-air computation for federated learning -- 10.1 Blind over-the-air computation -- 10.2 Problem formulation -- 10.3 Wirtinger flow algorithm for blind over-the-air computation -- 10.3.1 Wirtinger flow -- 10.3.2 Initialization strategies -- 10.4 Numerical results -- 10.5 Summary -- References -- Part 4 Final part: conclusions and future directions -- 11 Conclusions and future directions -- 11.1 Conclusions -- 11.2 Discussions and future directions -- Index -- Back Cover.
Weitere Ausg.:
ISBN 9780128238172
Weitere Ausg.:
ISBN 0128238178
Sprache:
Englisch
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