Download or read book Distributed Learning Systems with First Order Methods written by Ji Liu and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.

Download or read book First order and Stochastic Optimization Methods for Machine Learning written by Guanghui Lan and published by Springer Nature. This book was released on 2020-05-15 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

Download or read book Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers written by Stephen Boyd and published by Now Publishers Inc. This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.

Download or read book Federated Learning written by Heiko Ludwig and published by Springer Nature. This book was released on 2022-07-07 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.

Download or read book Understanding Distributed Systems Second Edition written by Roberto Vitillo and published by Roberto Vitillo. This book was released on 2022-02-23 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to build distributed systems is hard, especially if they are large scale. It's not that there is a lack of information out there. You can find academic papers, engineering blogs, and even books on the subject. The problem is that the available information is spread out all over the place, and if you were to put it on a spectrum from theory to practice, you would find a lot of material at the two ends but not much in the middle. That is why I decided to write a book that brings together the core theoretical and practical concepts of distributed systems so that you don't have to spend hours connecting the dots. This book will guide you through the fundamentals of large-scale distributed systems, with just enough details and external references to dive deeper. This is the guide I wished existed when I first started out, based on my experience building large distributed systems that scale to millions of requests per second and billions of devices. If you are a developer working on the backend of web or mobile applications (or would like to be!), this book is for you. When building distributed applications, you need to be familiar with the network stack, data consistency models, scalability and reliability patterns, observability best practices, and much more. Although you can build applications without knowing much of that, you will end up spending hours debugging and re-architecting them, learning hard lessons that you could have acquired in a much faster and less painful way. However, if you have several years of experience designing and building highly available and fault-tolerant applications that scale to millions of users, this book might not be for you. As an expert, you are likely looking for depth rather than breadth, and this book focuses more on the latter since it would be impossible to cover the field otherwise. The second edition is a complete rewrite of the previous edition. Every page of the first edition has been reviewed and where appropriate reworked, with new topics covered for the first time.

Download or read book Developing Digital RF Memories and Transceiver Technologies for Electromagnetic Warfare written by Phillip E. Pace and published by Artech House. This book was released on 2022-05-31 with total page 920 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive resource and thorough treatment in the latest development of Digital RF Memory (DRFM) technology and their key role in maintaining dominance over the electromagnetic spectrum. Part I discusses the use of advanced technology to design transceivers for spectrum sensing using unmanned systems to dominate the electromagnetic spectrum. Part II uses artificial intelligence and machine learning to enable modern spectrum sensing and detection signal processing for electronic support and electronic attack. Another key contribution is examination of counter-DRFM techniques. DRFM and transceiver design details and examples are provided along with the MATLAB software allowing the reader to construct their own embedded DRFM transceivers for unmanned systems. It examines the design trade-offs in developing multiple, structured, false target synthesis DRFM architectures and aids in developing counter-DRFM techniques and distinguish false target from real ones. Written by an expert in the field, and including MATLAB™ design software, this is the only comprehensive book written on the subject of DRFM.

Download or read book On Structured and Distributed Learning written by Rashish Tandon and published by . This book was released on 2017 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growth in size and complexity of data, methods exploiting low-dimensional structure, as well as distributed methods, have been playing an ever important role in machine learning. These approaches offer a natural choice to alleviate the computational burden, albeit typically at a statistical trade-off. In this thesis, we show that a careful utilization of structure of a problem, or bottlenecks of a distributed system, can also provide a statistical advantage in such settings. We do this from the purview of the following three problems: 1. Learning Graphical models with a few hubs: Graphical models are a popular tool to represent multivariate distributions. The task of learning a graphical model entails estimating the graph of conditional dependencies between variables. Existing approaches to learn graphical models require a number of samples polynomial in the maximum degree of the true graph, which can be large even if there are a few high-degree nodes. In this part of the thesis, we propose an estimator that detects and then ignores high degree nodes. Consequently, we show that such an estimator has a lower sample complexity requirement for learning the overall graph when the true graph has a few high-degree nodes or "hubs" for e.g. scale-free graphs. 2. Kernel Ridge Regression via partitioning: Kernel methods find wide and varied applicability in machine learning. However, solving the Kernel Ridge Regression (KRR) optimization requires computation that is cubic in the number of samples. In this work, we consider a divide-and-conquer approach to solve the KRR problem. The division step involves splitting the samples based on a partitioning of the input space, and the conquering step is to simply use the local KRR estimate in each partition. We show that this can not only lower the computational requirements of solving the KRR problem, but also lead to improved accuracy over both a single KRR estimate, and estimates based on random data partitioning. 3. Stragglers in Distributed Synchronous Gradient Descent: Synchronous methods in machine learning have many desirable properties, but they are only as fast as the slowest machine in a distributed system. The straggler/slow machine problem is a critical bottleneck for such methods. In this part of our work, we propose a novel framework based on Coding Theory for mitigating stragglers in Distributed Synchronous Gradient Descent (and its variants). Our approach views stragglers as errors/erasures. By carefully replicating data blocks and coding across gradients, we show how this can provide tolerance to failures and stragglers without incurring any communication overheads.

Download or read book Distributed Optimization in Networked Systems written by Qingguo Lü and published by Springer Nature. This book was released on 2023-02-08 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.

Download or read book Rollout Policy Iteration and Distributed Reinforcement Learning written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2021-08-20 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to develop in greater depth some of the methods from the author's Reinforcement Learning and Optimal Control recently published textbook (Athena Scientific, 2019). In particular, we present new research, relating to systems involving multiple agents, partitioned architectures, and distributed asynchronous computation. We pay special attention to the contexts of dynamic programming/policy iteration and control theory/model predictive control. We also discuss in some detail the application of the methodology to challenging discrete/combinatorial optimization problems, such as routing, scheduling, assignment, and mixed integer programming, including the use of neural network approximations within these contexts. The book focuses on the fundamental idea of policy iteration, i.e., start from some policy, and successively generate one or more improved policies. If just one improved policy is generated, this is called rollout, which, based on broad and consistent computational experience, appears to be one of the most versatile and reliable of all reinforcement learning methods. In this book, rollout algorithms are developed for both discrete deterministic and stochastic DP problems, and the development of distributed implementations in both multiagent and multiprocessor settings, aiming to take advantage of parallelism. Approximate policy iteration is more ambitious than rollout, but it is a strictly off-line method, and it is generally far more computationally intensive. This motivates the use of parallel and distributed computation. One of the purposes of the monograph is to discuss distributed (possibly asynchronous) methods that relate to rollout and policy iteration, both in the context of an exact and an approximate implementation involving neural networks or other approximation architectures. Much of the new research is inspired by the remarkable AlphaZero chess program, where policy iteration, value and policy networks, approximate lookahead minimization, and parallel computation all play an important role.

Download or read book Optimization and Applications written by Nicholas N. Olenev and published by Springer Nature. This book was released on 2021-11-04 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Optimization and Applications, OPTIMA 2021, held in Petrovac, Montenegro, in September-October 2021. The 22 full and 3 short papers presented were carefully reviewed and selected from 63 submissions. The papers are organized into the following topical sub-headings: mathematical programming, global optimization, discrete and combinatorial optimization, optimal control, optimization and data analysis, and game theory and mathematical economics.

Download or read book Distributed Systems written by Maarten van Steen and published by Createspace Independent Publishing Platform. This book was released on 2017-02 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: For this third edition of -Distributed Systems, - the material has been thoroughly revised and extended, integrating principles and paradigms into nine chapters: 1. Introduction 2. Architectures 3. Processes 4. Communication 5. Naming 6. Coordination 7. Replication 8. Fault tolerance 9. Security A separation has been made between basic material and more specific subjects. The latter have been organized into boxed sections, which may be skipped on first reading. To assist in understanding the more algorithmic parts, example programs in Python have been included. The examples in the book leave out many details for readability, but the complete code is available through the book's Website, hosted at www.distributed-systems.net. A personalized digital copy of the book is available for free, as well as a printed version through Amazon.com.

Download or read book Innovative Methods and Techniques in New Electric Power Systems written by David Gao and published by Frontiers Media SA. This book was released on 2023-04-03 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book High Dimensional Optimization and Probability written by Ashkan Nikeghbali and published by Springer Nature. This book was released on 2022-08-04 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces. The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas. Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Download or read book Architectures for Distributed and Complex M Learning Systems Applying Intelligent Technologies written by Caball‚, Santi and published by IGI Global. This book was released on 2009-10-31 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores state-of-the-art software architectures and platforms used to support distributed and mobile e-learning systems.

Download or read book Control Systems and Reinforcement Learning written by Sean Meyn and published by Cambridge University Press. This book was released on 2022-06-09 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Download or read book Mathematical Optimization Theory and Operations Research Recent Trends written by Yury Kochetov and published by Springer Nature. This book was released on 2022-09-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes refereed proceedings of the 21st International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2022, held in Petrozavodsk, Russia, in July 2022. The 21 full papers and 3 short papers presented in this volume were carefully reviewed and selected from a total of 88 submissions. The papers in the volume are organised according to the following topical headings: ​invited talks; integer programming and combinatorial optimization; mathematical programming; game theory and optimal control; operational research applications.

Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”