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Federated Learning
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-th...
Federated Learning
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 l...
Federated Learning
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-th...
Security, Privacy, and Digital Forensics in the Cloud
In a unique and systematic way, this book discusses the security and privacy aspects of the cloud, and the relevant cloud forensics. Cloud computing is an emerging yet revolutionary technology that has been changing the way people live and work. However, with the continuous growth of cloud computing and related services, security and privacy has become a critical issue. Written by some of the top experts in the field, this book specifically discusses security and privacy of the cloud, as well as the digital forensics of cloud data, applications, and services. The first half of the book enables readers to have a comprehensive understanding and background of cloud security, which will help the...
Digital Transformation for a Sustainable Society in the 21st Century
This book constitutes papers from the workshops held at the 18th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2019, which took place in Trondheim, Norway, in September 2019. The 11 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 33 submissions to the following workshops: DTIS: Digital Transformation for an Inclusive Society TPSIE: Trust and Privacy Aspects of Smart Information Environments 3(IT): Innovative Teaching of Introductory Topics in Information Technology CROPS: CROwd-Powered e-Services
Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development
This book focuses on different algorithms and models related to AI, big data and IoT used for various domains. It enables the reader to have a broader and deeper understanding of several perspectives regarding the dynamics, challenges, and opportunities for sustainable development using artificial intelligence, big data and IoT. Applications of Artificial Intelligence, Big Data and Internet of Things (IoT) in Sustainable Development focuses on IT-based advancements in multidisciplinary fields such as healthcare, finance, bioinformatics, industrial automation, and environmental science. The authors discuss the key issues of security, management, and the realization of possible solutions to hu...
Information Systems Engineering in Responsible Information Systems
This book constitutes the thoroughly refereed proceedings of the CAiSE Forum 2019 held in Rome, Italy, as part of the 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019, in June 2019. The CAiSE Forum - one of the traditional tracks of the CAiSE conference - aims to present emerging new topics and controversial positions, as well as demonstration of innovative systems, tools and applications related to information systems engineering. This year’s theme was “Responsible Information Systems”. The 19 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 14 direct submissions (of which 7 full papers were selected), plus 15 transfers from the CAiSE main conference (which resulted in another 12 full and 3 short papers).
Collaborative Networks of Cognitive Systems
This book constitutes the refereed proceedings of the 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018, held in Cardiff, UK, in September 2018. The 57 revised full papers were carefully reviewed and selected from 143 submissions. They provide a comprehensive overview of identified challenges and recent advances in various collaborative network (CN) domains and their applications, with a strong focus on the following areas: blockchain in collaborative networks, industry transformation and innovation, semantics in networks of cognitive systems, cognitive systems for resilience management, collaborative energy services in smart cities, cognitive systems in agribusiness, building information modeling, industry 4.0 support frameworks, health and social welfare services, risk, privacy and security, collaboration platform issues, sensing, smart and sustainable enterprises, information systems integration, dynamic logistics networks, collaborative business processes, value creation in networks, users and organizations profiling, and collaborative business strategies.
Malware Analysis Using Artificial Intelligence and Deep Learning
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
