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 Federated Learning written by Liefeng Bo and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorithms and finally system implementation in three parts. It provides a comprehensive review and summary of federated learning technology, as well as presenting numerous novel federated learning algorithms which no other books have summarized. The work also references the most recent papers, articles and reviews from the past several years to keep pace with the academic and industrial state of the art of federated learning. The first part lays a foundational understanding of federated learning by going through its definition and characteristics, and also possible application scenarios and related privacy protection technologies. The second part elaborates on some of the federated learning algorithms innovated by JD Technology which encompass both vertical and horizontal scenarios, including vertical federated tree models, linear regression, kernel learning, asynchronous methods, deep learning, homomorphic encryption, and reinforcement learning. The third and final part shifts in scope to federated learning systems - namely JD Technology's own FedLearn system - by discussing its design and implementation using gRPC, in addition to specific performance optimization techniques plus integration with blockchain technology. This book will serve as a great reference for readers who are experienced in federated learning algorithms, building privacy-preserving machine learning applications or solving real-world problems with privacy-restricted scenarios.

Download or read book Federated Learning written by Yaochu Jin and published by Springer Nature. This book was released on 2022-11-29 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.

Download or read book Federated Learning written by Lam M. Nguyen and published by Elsevier. This book was released on 2024-02-09 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning: Theory and Practice provides a holistic treatment to federated learning, starting with a broad overview on federated learning as a distributed learning system with various forms of decentralized data and features. A detailed exposition then follows of core challenges and practical modeling techniques and solutions, spanning a variety of aspects in communication efficiency, theoretical convergence and security, viewed from different perspectives. Part II features emerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service, and Part III and IV present a wide array of industrial applications of federated learning, including potential venues and visions for federated learning in the near future. This book provides a comprehensive and accessible introduction to federated learning which is suitable for researchers and students in academia and industrial practitioners who seek to leverage the latest advances in machine learning for their entrepreneurial endeavors Presents the fundamentals and a survey of key developments in the field of federated learning Provides emerging, state-of-the art topics that build on fundamentals Contains industry applications Gives an overview of visions of the future

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.”

Download or read book Handbook on Federated Learning written by Saravanan Krishnan and published by CRC Press. This book was released on 2024-01-09 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.

Download or read book Federated Learning written by Jayakrushna Sahoo and published by CRC Press. This book was released on 2024-09-20 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well. The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems. The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included. This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.

Download or read book Federated Learning written by Yang Qiang (author) and published by . This book was released on 1901 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development of a Federated Learning Software written by Utsha Khondkar and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, research in AI (artificial intelligence) related technologies have been evolving at an ever growing rate. One of the reasons is evolution in computational resources that have enabled training deep neural network models that existed for a long time yet did not get as much attention as today due to the computational constraint. Research spans from core theoretical work that ensures generalizability and functionality of deep learning models, to cutting edge applications in domains of natural language processing, computer vision, speech recognition, reinforcement learning, and so on. The industry is also putting enormous funding and effort in search of better performing AI models and framework. In this context, federated machine learning has emerged by introducing a new point of view, to focus on deep learning models deployed to edge devices. Edge devices are usually more resource-constrained compared to large scale computing clusters, thus it has been explored how to best aggregate locally trained models with potentially private or different local dataset to construct a globally useful AI model. Furthermore, different federated learning schemes modify interaction patterns of edge devices or induce sparsity in models and datasets to enforce efficiency in communication. To support evaluation of federated learning algorithms, I designed and developed a software platform capable of running federated learning experiments and providing useful analytics to evaluate newly proposed fedearation schemes. I have made careful design choices and used implementation techniques inspired by experience from object oriented software development, which enables scalability, modularity, expandability and maintainability. The developed software adheres to those proposed objectives, showing robustness and flexibility to adopt to newly proposed decentralized federated learning schemes and provides meaningful experiments

Download or read book Federated Learning written by M. Irfan Uddin and published by CRC Press. This book was released on 2024-09-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated 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

Download or read book Advances and Open Problems in Federated Learning written by Peter Kairouz and published by . This book was released on 2021-06-23 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Download or read book Federated Learning written by Heiko Ludwig and published by . This book was released on 2022 with total page 0 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. The first part addresses algorithmic questions of solving different machine learning tasks in a federated way and 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 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 Federated Learning Systems written by Muhammad Habib ur Rehman and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.

Download or read book Applications in Ubiquitous Computing written by Raman Kumar and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a deep dive into ubiquitous computing for applications in health, business, education, tourism, and transportation. The rich interdisciplinary contents of the book appeal to readers from diverse disciplines who aspire to create new and innovative research initiatives and applications in ubiquitous computing. Topics include condition monitoring and diagnostics; multi-objective optimization in design, multi-objective optimization of machining parameters, and more. The book benefits researchers, advanced students, as well as practitioners interested in applications of ubiquitous computing. Features practical, tested applications in ubiquitous computing Includes applications such as health, business, education, electronics, tourism, and transportation Applicable to researchers, academics, students, and professionals.

Download or read book The Conduct of Science written by Michael W. Friedlander and published by Prentice Hall. This book was released on 1972 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Next Generation Data Technologies for Collective Computational Intelligence written by Nik Bessis and published by Springer Science & Business Media. This book was released on 2011-04-28 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on next generation data technologies in support of collective and computational intelligence. The book brings various next generation data technologies together to capture, integrate, analyze, mine, annotate and visualize distributed data – made available from various community users – in a meaningful and collaborative for the organization manner. A unique perspective on collective computational intelligence is offered by embracing both theory and strategies fundamentals such as data clustering, graph partitioning, collaborative decision making, self-adaptive ant colony, swarm and evolutionary agents. It also covers emerging and next generation technologies in support of collective computational intelligence such as Web 2.0 social networks, semantic web for data annotation, knowledge representation and inference, data privacy and security, and enabling distributed and collaborative paradigms such as P2P, Grid and Cloud Computing due to the geographically dispersed and distributed nature of the data. The book aims to cover in a comprehensive manner the combinatorial effort of utilizing and integrating various next generations collaborative and distributed data technologies for computational intelligence in various scenarios. The book also distinguishes itself by assessing whether utilization and integration of next generation data technologies can assist in the identification of new opportunities, which may also be strategically fit for purpose.

Download or read book City Diplomacy written by Sohaela Amiri and published by Springer Nature. This book was released on 2020-07-11 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume provides an inclusive explanation of what, why, and how cities interact with global counterparts as well as with nation states, non-governmental organizations, and foreign publics. The chapters present theoretical and analytical approaches to the study of city diplomacy as well as case studies to capture the nuances of the practice. By bringing together a diverse group of authors in terms of their geographic location, academic and practitioner backgrounds, the volume speaks to multiple disciplines, including diplomacy, political science, communication, sociology, marketing and tourism.