Federated And Transfer Learning

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Federated And Transfer Learning
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Author : Roozbeh Razavi-Far
language : en
Publisher: Springer Nature
Release Date : 2022-09-30
Federated And Transfer Learning written by Roozbeh Razavi-Far and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Technology & Engineering categories.
This 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 And Transfer Learning Applications
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Author : George Drosatos
language : en
Publisher:
Release Date : 2024-04
Federated And Transfer Learning Applications written by George Drosatos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04 with Technology & Engineering categories.
The classic example of machine learning is based on isolated learning-a single model for each task using a single dataset. Most deep learning methods require a significant amount of labeled data, preventing their applicability in many areas where there is a shortage. In these cases, the ability of models to leverage information from unlabeled data or data that are not publicly available (for privacy and security reasons) can offer a remarkable alternative. Transfer learning and federated learning are alternative approaches that have emerged in recent years. More precisely, transfer learning is defined as the set of methods that leverage data from additional fields or tasks to train a model with greater generalizability and usually use a smaller amount of labeled data (via fine-tuning) to make them more specific for dedicated tasks. Accordingly, federated learning is a learning model that seeks to address the problem of data management and privacy through joint training with these data without the need to transfer the data to a central entity. With this in mind, this Special Issue of Applied Sciences provides an overview of the latest developments in this field.
Federated Learning Systems
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Author : Muhammad Habib ur Rehman
language : en
Publisher: Springer Nature
Release Date : 2021-06-11
Federated Learning Systems written by Muhammad Habib ur Rehman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Technology & Engineering categories.
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
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
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.”
Practical Deep Learning For Cloud Mobile And Edge
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Author : Anirudh Koul
language : en
Publisher: O'Reilly Media
Release Date : 2019-10-14
Practical Deep Learning For Cloud Mobile And Edge written by Anirudh Koul and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Transfer Learning
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Author : Qiang Yang
language : en
Publisher: Cambridge University Press
Release Date : 2020-02-13
Transfer Learning written by Qiang Yang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Computers categories.
This in-depth tutorial for students, researchers, and developers covers foundations, plus applications ranging from search to multimedia.
Introduction To Transfer Learning
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Author : Jindong Wang
language : en
Publisher: Springer Nature
Release Date : 2023-03-30
Introduction To Transfer Learning written by Jindong Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-30 with Computers categories.
Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
Security And Privacy In Digital Economy
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Author : Shui Yu
language : en
Publisher: Springer
Release Date : 2020-10-17
Security And Privacy In Digital Economy written by Shui Yu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-17 with Computers categories.
This book constitutes the refereed proceedings of the First International Conference on Security and Privacy in Digital Economy, SPDE 2020, held in Quzhou, China, in October 2020*. The 49 revised full papers and 2 short papers were carefully reviewed and selected from 132 submissions. The papers are organized in topical sections: cyberspace security, privacy protection, anomaly and intrusion detection, trust computation and forensics, attacks and countermeasures, covert communication, security protocol, anonymous communication, security and privacy from social science. *The conference was held virtually due to the COVID-19 pandemic.
Transfer Learning
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2025-03-12
Transfer Learning written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-12 with Computers categories.
This edited volume explores the potential of transfer learning in advancing artificial intelligence (AI) across diverse domains. Transfer learning enables AI systems to leverage knowledge gained from one task to enhance performance in another, significantly reducing data requirements and training time while improving model efficiency. The book presents the latest approaches for implementing transfer learning in various contexts, from telecommunications and brain-computer interfaces to quantum computing applications. Readers will discover innovative techniques for domain adaptation, cross-domain knowledge transfer, and hybrid classical-quantum implementations. The text addresses critical challenges in making transfer learning more explainable, reliable, and scalable, particularly concerning privacy preservation and computational efficiency. Key topics include AI-native networks, neural network transfer learning, domain adaptation strategies, and quantum machine learning integration. Both theoretical frameworks and practical implementations are discussed, making this book valuable for researchers, practitioners, and students interested in developing more efficient and capable AI systems. The content bridges the gap between theoretical understanding and practical application, offering insights into how transfer learning can be effectively deployed in real-world scenarios. By examining transfer learning through multiple lenses, from traditional neural networks to quantum computing, this volume provides a unique perspective on the future of AI development and its potential to revolutionize various technological sectors.
Applied Soft Computing And Communication Networks
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Author : Sabu M. Thampi
language : en
Publisher: Springer Nature
Release Date : 2024-07-27
Applied Soft Computing And Communication Networks written by Sabu M. Thampi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Computers categories.
This book constitutes thoroughly refereed post-conference proceedings of the International Applied Soft Computing and Communication Networks (ACN 2023) held at PES University, Bangalore, India, during December 18–20, 2023. The research papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on security and privacy, network management and software-defined networks, Internet of Things (IoT) and cyber-physical systems, intelligent distributed systems, mobile computing and vehicle communications, and emerging topics. The book is directed to the researchers and scientists engaged in various fields of intelligent systems.