Federated Learning And Its Role In The Privacy Preservation Of Iot Devices

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Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Author : Tanweer Alam
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Federated Learning and Its Role in the Privacy Preservation of IoT Devices by : Tanweer Alam

Download or read book Federated Learning and Its Role in the Privacy Preservation of IoT Devices written by Tanweer Alam and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.

Federated Learning for IoT Applications

Author : Satya Prakash Yadav
Publisher : Springer Nature
ISBN 13 : 3030855597
Total Pages : 269 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Federated Learning for IoT Applications by : Satya Prakash Yadav

Download or read book Federated Learning for IoT Applications written by Satya Prakash Yadav and published by Springer Nature. This book was released on 2022-02-02 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.

Handbook on Federated Learning

Author : Saravanan Krishnan
Publisher : CRC Press
ISBN 13 : 1003837522
Total Pages : 381 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Handbook on Federated Learning by : Saravanan Krishnan

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.

Privacy Preservation in IoT: Machine Learning Approaches

Author : Youyang Qu
Publisher : Springer Nature
ISBN 13 : 9811917973
Total Pages : 127 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Privacy Preservation in IoT: Machine Learning Approaches by : Youyang Qu

Download or read book Privacy Preservation in IoT: Machine Learning Approaches written by Youyang Qu and published by Springer Nature. This book was released on 2022-04-27 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.

Federated Learning

Author : Qiang Yang
Publisher : Springer Nature
ISBN 13 : 3030630765
Total Pages : 291 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Federated Learning by : Qiang Yang

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.”

Federated Learning

Author : Jayakrushna Sahoo
Publisher : CRC Press
ISBN 13 : 1040088597
Total Pages : 353 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Federated Learning by : Jayakrushna Sahoo

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.

Federated Learning for Internet of Medical Things

Author : Pronaya Bhattacharya
Publisher : CRC Press
ISBN 13 : 1000891399
Total Pages : 254 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Federated Learning for Internet of Medical Things by : Pronaya Bhattacharya

Download or read book Federated Learning for Internet of Medical Things written by Pronaya Bhattacharya and published by CRC Press. This book was released on 2023-06-16 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.

Deep Learning Techniques for IoT Security and Privacy

Author : Mohamed Abdel-Basset
Publisher : Springer Nature
ISBN 13 : 3030890252
Total Pages : 273 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Deep Learning Techniques for IoT Security and Privacy by : Mohamed Abdel-Basset

Download or read book Deep Learning Techniques for IoT Security and Privacy written by Mohamed Abdel-Basset and published by Springer Nature. This book was released on 2021-12-05 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.

Federated Learning

Author : Heiko Ludwig
Publisher : Springer Nature
ISBN 13 : 3030968960
Total Pages : 531 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Federated Learning by : Heiko Ludwig

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.

Federated Learning

Author : M. Irfan Uddin
Publisher : CRC Press
ISBN 13 : 1040115330
Total Pages : 194 pages
Book Rating : 4.0/5 (41 download)

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Book Synopsis Federated Learning by : M. Irfan Uddin

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

Federated Learning and Privacy-Preserving in Healthcare AI

Author : Lilhore, Umesh Kumar
Publisher : IGI Global
ISBN 13 :
Total Pages : 373 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Federated Learning and Privacy-Preserving in Healthcare AI by : Lilhore, Umesh Kumar

Download or read book Federated Learning and Privacy-Preserving in Healthcare AI written by Lilhore, Umesh Kumar and published by IGI Global. This book was released on 2024-05-02 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.

Federated Learning Systems

Author : Muhammad Habib ur Rehman
Publisher : Springer Nature
ISBN 13 : 3030706044
Total Pages : 207 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Federated Learning Systems by : Muhammad Habib ur Rehman

Download or read book Federated Learning Systems written by Muhammad Habib ur Rehman and published by Springer Nature. This book was released on 2021-06-11 with total page 207 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.

Federated Learning Techniques And Its Application In The Healthcare Industry

Author : H L Gururaj
Publisher : World Scientific
ISBN 13 : 9811287953
Total Pages : 235 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Federated Learning Techniques And Its Application In The Healthcare Industry by : H L Gururaj

Download or read book Federated Learning Techniques And Its Application In The Healthcare Industry written by H L Gururaj and published by World Scientific. This book was released on 2024-05-28 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning is currently an emerging technology in the field of machine learning. Federated Learning is a structure which trains a centralized model for a given assignment, where the data is de-centralized across different edge devices or servers. This enables preservation of the confidentiality of data on various edge devices, as only the updated outcomes of the models are shared with the centralized model. This means the data can remain on each edge device, while we can still train a model using that data.Federated Learning has greatly increased the potential to transmute data in the healthcare industry, enabling healthcare professionals to improve treatment of patients.This book comprises chapters on applying Federated models in the field of healthcare industry.Federated Learning mainly concentrates on securing the privacy of data by training local data in a shared global model without putting the training data in a centralized location. The importance of federated learning lies in its innumerable uses in health care that ranges from maintaining the privacy of raw data of the patients, discover clinically alike patients, forecasting hospitalization due to cardiac events impermanence and probable solutions to the same. The goal of this edited book is to provide a reference guide to the theme.

Deep Learning Approaches for Security Threats in IoT Environments

Author : Mohamed Abdel-Basset
Publisher : John Wiley & Sons
ISBN 13 : 1119884144
Total Pages : 388 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Deep Learning Approaches for Security Threats in IoT Environments by : Mohamed Abdel-Basset

Download or read book Deep Learning Approaches for Security Threats in IoT Environments written by Mohamed Abdel-Basset and published by John Wiley & Sons. This book was released on 2022-12-20 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.

Security and Privacy Preserving for IoT and 5G Networks

Author : Ahmed A. Abd El-Latif
Publisher : Springer Nature
ISBN 13 : 3030854280
Total Pages : 283 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Security and Privacy Preserving for IoT and 5G Networks by : Ahmed A. Abd El-Latif

Download or read book Security and Privacy Preserving for IoT and 5G Networks written by Ahmed A. Abd El-Latif and published by Springer Nature. This book was released on 2021-10-09 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art research on security and privacy- preserving for IoT and 5G networks and applications. The accepted book chapters covered many themes, including traceability and tamper detection in IoT enabled waste management networks, secure Healthcare IoT Systems, data transfer accomplished by trustworthy nodes in cognitive radio, DDoS Attack Detection in Vehicular Ad-hoc Network (VANET) for 5G Networks, Mobile Edge-Cloud Computing, biometric authentication systems for IoT applications, and many other applications It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on security and privacy- preserving for IoT and 5G networks.

Federated Learning Systems

Author : Muhammad Habib ur Rehman
Publisher :
ISBN 13 : 9783030706050
Total Pages : 0 pages
Book Rating : 4.7/5 (6 download)

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Book Synopsis Federated Learning Systems by : Muhammad Habib ur Rehman

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.

Privacy-Preserving Deep Learning

Author : Kwangjo Kim
Publisher : Springer Nature
ISBN 13 : 9811637644
Total Pages : 81 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Privacy-Preserving Deep Learning by : Kwangjo Kim

Download or read book Privacy-Preserving Deep Learning written by Kwangjo Kim and published by Springer Nature. This book was released on 2021-07-22 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.