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Federated AI for Real-World Business Scenarios
by Dinesh C. VermaThis book provides an overview of Federated Learning and how it can be used to build real-world AI-enabled applications. Real-world AI applications frequently have training data distributed in many different locations, with data at different sites having different properties and different formats. In many cases, data movement is not permitted due to security concerns, bandwidth, cost or regulatory restriction. Under these conditions, techniques of federated learning can enable creation of practical applications. Creating practical applications requires implementation of the cycle of learning from data, inferring from data, and acting based on the inference. This book will be the first one to cover all stages of the Learn-Infer-Act cycle, and presents a set of patterns to apply federation to all stages. Another distinct feature of the book is the use of real-world applications with an approach that discusses all aspects that need to be considered in an operational system, including handling of data issues during federation, maintaining compliance with enterprise security policies, and simplifying the logistics of federated AI in enterprise contexts. The book considers federation from a manner agnostic to the actual AI models, allowing the concepts to be applied to all varieties of AI models. This book is probably the first one to cover the space of enterprise AI-based applications in a holistic manner.
Federated and Transfer Learning (Adaptation, Learning, and Optimization #27)
by Roozbeh Razavi-Far Boyu Wang Matthew E. Taylor Qiang YangThis book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Federated Cyber Intelligence: Federated Learning for Cybersecurity (SpringerBriefs in Computer Science)
by Hamed Tabrizchi Ali AghasiThis book offers a detailed exploration of how federated learning can address critical challenges in modern cybersecurity. It begins with an introduction to the core principles of federated learning. Then it highlights a strong foundation by exploring the fundamental components, workflow, and algorithms of federated learning, alongside its historical development and relevance in safeguarding digital systems. The subsequent sections offer insight into key cybersecurity concepts, including confidentiality, integrity, and availability. It also offers various types of cyber threats, such as malware, phishing, and advanced persistent threats. This book provides a practical guide to applying federated learning in areas such as intrusion detection, malware detection, phishing prevention, and threat intelligence sharing. It examines the unique challenges and solutions associated with this approach, such as data heterogeneity, synchronization strategies and privacy-preserving techniques. This book concludes with discussions on emerging trends, including blockchain, edge computing and collaborative threat intelligence. This book is an essential resource for researchers, practitioners and decision-makers in cybersecurity and AI.
Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities (Advances in Smart Healthcare Technologies)
by Amandeep Kaur Chetna Kaushal Mehedi Hassan Si Thu AungThis book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.
Federated Learning: Fundamentals and Advances (Machine Learning: Foundations, Methodologies, and Applications)
by Yaochu Jin Hangyu Zhu Jinjin Xu Yang ChenThis book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.
Federated Learning: A Comprehensive Overview of Methods and Applications
by Heiko Ludwig Nathalie BaracaldoFederated 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.
Federated Learning: Unlocking the Power of Collaborative Intelligence (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)
by M. Irfan Uddin and Wali Khan MashwaniFederated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations.With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios – such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems.Key Features: Provides a comprehensive guide on tools and techniques of federated learning Highlights many practical real-world examples Includes easy-to-understand explanations
Federated Learning: Privacy and Incentive (Lecture Notes in Computer Science #12500)
by Qiang Yang Lixin Fan Han YuThis 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.”
Federated Learning for Future Intelligent Wireless Networks
by Yao Sun Chaoqun You Gang Feng Lei ZhangFederated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL Comprehensive explorations of wireless communication network design and optimization for federated learning Practical discussions of novel federated learning algorithms and frameworks for future wireless networks Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.
Federated Learning for Internet of Medical Things: Concepts, Paradigms, and Solutions
by Pronaya Bhattacharya Ashwin Verma Sudeep TanwarThis book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning. The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.
Federated Learning for IoT Applications (EAI/Springer Innovations in Communication and Computing)
by Sachin Kumar Satya Prakash Yadav Dharmendra Prasad Mahato Bhoopesh Singh BhatiThis book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.
Federated Learning for Neural Disorders in Healthcare 6.0 (Future Generation Information Systems)
by Anindya Nag Reddy C, Kishor KumarThis reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.
Federated Learning for Smart Communication using IoT Application (Chapman & Hall/CRC Cyber-Physical Systems)
by Kaushal Kishor Parma Nand Vishal Jain Neetesh Saxena Gaurav Agarwal Rani AstyaThe effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirementsis demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.Features:• Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy.• Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy.• Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area.• Analyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applications.• Comprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapter.This book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.
Federated Learning for Wireless Networks (Wireless Networks)
by Choong Seon Hong Latif U. Khan Mingzhe Chen Dawei Chen Walid Saad Zhu HanRecently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
Federated Learning in the Age of Foundation Models - FL 2024 International Workshops: FL@FM-WWW 2024, Singapore, May 14, 2024; FL@FM-ICME 2024, Niagara Falls, ON, Canada, July 15, 2024; FL@FM-IJCAI 2024, Jeju Island, South Korea, August 5, 2024; and FL@FM-NeurIPS 2024, Vancouver, BC, Canada, December 15, 2024, Revised Selected Papers (Lecture Notes in Computer Science #15501)
by Han Yu Xiaoxiao Li Zenglin Xu Randy Goebel Irwin KingThis LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows: FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024. This LNAI volume focuses on the following topics: Efficient Model Adaptation and Personalization, Data Heterogeneity and Incomplete Data, Integration of Specialized Neural Architectures, Frameworks and Tools for Federated Learning, Applications in Domain-Specific Contexts, Unsupervised and Lightweight Learning, and Causal Discovery and Black-Box Optimization.
Federated Learning Over Wireless Edge Networks (Wireless Networks)
by Wei Yang Lim Jer Shyuan Ng Zehui Xiong Dusit Niyato Chunyan MiaoThis book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.
Federated Learning Systems: Towards Next-Generation AI (Studies in Computational Intelligence #965)
by Muhammad Habib ur Rehman Mohamed Medhat GaberThis book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks
by Kiyoshi Nakayama PhD George JenoLearn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.
Fëdor Khitruk: A Look at Soviet Animation through the Work of One Master
by Laura PontieriThis book is a first and long-awaited study of the directorial work of the animation master Fëdor Khitruk (1917–2012), an artist who formed in the tradition of classical cel animation only to break the conventions once he turned into a director; a liaison between artists and authorities; a personality who promoted daring films to be created in the Soviet Union dominated by socialist realism; and a teacher and supporter of young artists that continued to carry on his legacy long after the Soviet empire collapsed. Fëdor Khitruk: A Look at Soviet Animation through the Work of One Master reveals Khitruk’s mastery in the art of the moving image and his critical role as a director of films that changed the look of Soviet animation and its relation to the animation world within and beyond the Eastern Bloc. Based on archival research, personal interviews, published memoirs, and perceptive analyses of Khitruk’s production of films for children and adults, this study is a must-read for scholars in Soviet art and culture as well as readers fascinated by traditional animation art.
Fedora Bible 2011 Edition
by Eric Foster-Johnson Christopher NegusGet all the essentials of the major changes in Fedora 14Veteran authors Christopher Negus and Eric Foster-Johnson provide you with a thorough look at the skills needed to master the latest version of Fedora and Red Hat Linux. Their step-by-step instructions walk you through a painless and simple installation of Linux; then you'll explore the major changes to the release of Fedora 14 while also revisiting the previous version so you can see what features have been updated and revised.Focuses on the essentials of the updated and new elements of Fedora Linux 14Addresses using packagekit, running Windows apps, scanning images, and installing over the InternetTouches on how to work in a Linux office with MSFT office compatible office appsCovers new material on zarafa, xenner, deja dup, and moreFeatures a DVD that includes the latest distribution of Fedora Linux as well as a bootable Fedora LiveCDFedora 14 includes many important updates and additions -- this book gets you up to date on the most essential changes.
Fedora Linux: A Complete Guide to Red Hat's Community Distribution
by Chris Tyler"Neither a "Starting Linux" book nor a dry reference manual, this book has a lot to offer to those coming to Fedora from other operating systems or distros."-- Behdad Esfahbod, Fedora developerThis book will get you up to speed quickly on Fedora Linux, a securely-designed Linux distribution that includes a massive selection of free software packages. Fedora is hardened out-of-the-box, it's easy to install, and extensively customizable - and this book shows you how to make Fedora work for you.Fedora Linux: A Complete Guide to Red Hat's Community Distribution will take you deep into essential Fedora tasks and activities by presenting them in easy-to-learn modules. From installation and configuration through advanced topics such as administration, security, and virtualization, this book captures the important details of how Fedora Core works--without the fluff that bogs down other books and help/how-to web sites. Instead, you can learn from a concise task-based approach to using Fedora as both a desktop and server operating system.In this book, you'll learn how to:Install Fedora and perform basic administrative tasksConfigure the KDE and GNOME desktopsGet power management working on your notebook computer and hop on a wired or wireless networkFind, install, and update any of the thousands of packages available for FedoraPerform backups, increase reliability with RAID, and manage your disks with logical volumesSet up a server with file sharing, DNS, DHCP, email, a Web server, and moreWork with Fedora's security features including SELinux, PAM, and Access Control Lists (ACLs)Whether you are running the stable version of Fedora Core or bleeding-edge Rawhide releases, this book has something for every level of user. The modular, lab-based approach not only shows you how things work-but also explains why--and provides you with the answers you need to get up and running with Fedora Linux.Chris Tyler is a computer consultant and a professor of computer studies at Seneca College in Toronto, Canada where he teaches courses on Linux and X Window System Administration. He has worked on systems ranging from embedded data converters to Multics mainframes.
Feed-Forward: On the Future of Twenty-First-Century Media
by Mark B. N. HansenEven as media in myriad forms increasingly saturate our lives, we nonetheless tend to describe our relationship to it in terms from the twentieth century: we are consumers of media, choosing to engage with it. In Feed-Forward, Mark B. N. Hansen shows just how outmoded that way of thinking is: media is no longer separate from us but has become an inescapable part of our very experience of the world. Drawing on the speculative empiricism of philosopher Alfred North Whitehead, Hansen reveals how new media call into play elements of sensibility that greatly affect human selfhood without in any way belonging to the human. From social media to data-mining to new sensor technologies, media in the twenty-first century work largely outside the realm of perceptual consciousness, yet at the same time inflect our every sensation. Understanding that paradox, Hansen shows, offers us a chance to put forward a radically new vision of human becoming, one that enables us to reground the human in a non-anthropocentric view of the world and our experience in it.
Feedback Arc Set: A History of the Problem and Algorithms (SpringerBriefs in Computer Science)
by Robert KudelićThe main aim of the book is to give a review of all relevant information regarding a well-known and important problem of Feedback Arc Set (FAS). This review naturally also includes a history of the problem, as well as specific algorithms. To this point such a work does not exist: There are sources where one can find incomplete and perhaps untrustworthy information. With this book, information about FAS can be found easily in one place: formulation, description, theoretical background, applications, algorithms etc. Such a compendium will be of help to people involved in research, but also to people that want to quickly acquaint themselves with the problem and need reliable information. Thus research, professional work and learning can proceed in a more streamlined and faster way.
Feedback Control for Computer Systems: Introducing Control Theory to Enterprise Programmers
by Philipp K. JanertHow can you take advantage of feedback control for enterprise programming? With this book, author Philipp K. Janert demonstrates how the same principles that govern cruise control in your car also apply to data center management and other enterprise systems. Through case studies and hands-on simulations, you’ll learn methods to solve several control issues, including mechanisms to spin up more servers automatically when web traffic spikes.Feedback is ideal for controlling large, complex systems, but its use in software engineering raises unique issues. This book provides basic theory and lots of practical advice for programmers with no previous background in feedback control.Learn feedback concepts and controller designGet practical techniques for implementing and tuning controllersUse feedback “design patterns” for common control scenariosMaintain a cache’s “hit rate” by automatically adjusting its sizeRespond to web traffic by scaling server instances automaticallyExplore ways to use feedback principles with queueing systemsLearn how to control memory consumption in a game engineTake a deep dive into feedback control theory
Feedback Control in Systems Biology
by Carlo Cosentino Declan BatesLike engineering systems, biological systems must also operate effectively in the presence of internal and external uncertainty-such as genetic mutations or temperature changes, for example. It is not surprising, then, that evolution has resulted in the widespread use of feedback, and research in systems biology over the past decade has shown that