Federated Learning With Python
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Federated Learning With Python
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Author : Kiyoshi Nakayama PhD
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-28
Federated Learning With Python written by Kiyoshi Nakayama PhD and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-28 with Computers categories.
Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level Key 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 Description Federated 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 for This 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.
Federated Learning In Practice
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Author : Kalen Virell
language : en
Publisher: Independently Published
Release Date : 2025-08-13
Federated Learning In Practice written by Kalen Virell and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-13 with Computers categories.
Are you ready to transform your understanding of AI and unlock the power of Federated Learning? In a world where data privacy is paramount, this breakthrough approach to training machine learning models across devices-without sharing raw data-is revolutionizing the future of artificial intelligence. Federated Learning in Practice: Training Across Devices Without Sharing Raw Data is the ultimate guide for anyone looking to master this cutting-edge technique. Whether you're a machine learning engineer, a privacy-conscious developer, or simply someone interested in the future of AI, this book will equip you with the knowledge and practical skills to build secure, scalable, and privacy-preserving machine learning systems. Inside, you'll learn: The fundamentals of federated learning and why it's the key to privacy-first AI How to implement federated learning systems across devices and environments Strategies to overcome challenges like data heterogeneity, device dropout, and unreliable networks Techniques to protect sensitive data using secure aggregation and differential privacy Real-world case studies from industries like healthcare, finance, and mobile AI Practical, hands-on examples and code in Python with frameworks like TensorFlow Federated and PySyft This book isn't just theory-it's a step-by-step roadmap that will show you how to take your AI projects from concept to deployment. You'll walk away with the tools and confidence to create models that learn collaboratively, while keeping user data private and secure. The future of AI is decentralized, privacy-focused, and more powerful than ever. Don't get left behind-buy this book now and learn how to be part of the next wave of innovation in machine learning.
Federated Learning From Algorithms To System Implementation
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Author : Liefeng Bo
language : en
Publisher: World Scientific
Release Date : 2024-08-16
Federated Learning From Algorithms To System Implementation written by Liefeng Bo and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-16 with Computers categories.
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.
Blockchain And Deep Learning For Smart Healthcare
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Author : Akansha Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2024-01-31
Blockchain And Deep Learning For Smart Healthcare written by Akansha Singh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
BLOCKCHAIN and DEEP LEARNING for SMART HEALTHCARE The book discusses the popular use cases and applications of blockchain technology and deep learning in building smart healthcare. The book covers the integration of blockchain technology and deep learning for making smart healthcare systems. Blockchain is used for health record-keeping, clinical trials, patient monitoring, improving safety, displaying information, and transparency. Deep learning is also showing vast potential in the healthcare domain. With the collection of large quantities of patient records and data, and a trend toward personalized treatments. there is a great need for automated and reliable processing and analysis of health information. This book covers the popular use cases and applications of both the above-mentioned technologies in making smart healthcare. Audience Comprises professionals and researchers working in the fields of deep learning, blockchain technology, healthcare & medical informatics. In addition, as the book provides insights into the convergence of deep learning and blockchain technology in healthcare systems and services, medical practitioners as well as healthcare professionals will find this essential reading.
Engineering Of Computer Based Systems
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Author : Jan Kofroň
language : en
Publisher: Springer Nature
Release Date : 2023-11-28
Engineering Of Computer Based Systems written by Jan Kofroň and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-28 with Computers categories.
This book constitutes the refereed proceedings of the 8th International Conference on Engineering of Computer-Based Systems, ECBS 2023, which was held in Västerås, Sweden, in October 2023. The 11 full papers included in this book were carefully reviewed and selected from 26 submissions and present software, hardware, and communication perspectives of systems engineering through its many facets. The special theme of this year is ”Engineering for Responsible AI“.
Practicing Trustworthy Machine Learning
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Author : Yada Pruksachatkun
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-03
Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Computers categories.
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
Machine Learning Deep Learning And Ai For Cybersecurity
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Author : Mark Stamp
language : en
Publisher: Springer Nature
Release Date : 2025-05-09
Machine Learning Deep Learning And Ai For Cybersecurity written by Mark Stamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-09 with Mathematics categories.
This book addresses a variety of problems that arise at the interface between AI techniques and challenging problems in cybersecurity. The book covers many of the issues that arise when applying AI and deep learning algorithms to inherently difficult problems in the security domain, such as malware detection and analysis, intrusion detection, spam detection, and various other subfields of cybersecurity. The book places particular attention on data driven approaches, where minimal expert domain knowledge is required. This book bridges some of the gaps that exist between deep learning/AI research and practical problems in cybersecurity. The proposed topics cover a wide range of deep learning and AI techniques, including novel frameworks and development tools enabling the audience to innovate with these cutting-edge research advancements in various security-related use cases. The book is timely since it is not common to find clearly elucidated research that applies the latest developments in AI to problems in cybersecurity.
International Conference On Advanced Intelligent Systems For Sustainable Development Ai2sd 2024
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Author : Mostafa Ezziyyani
language : en
Publisher: Springer Nature
Release Date : 2025-07-19
International Conference On Advanced Intelligent Systems For Sustainable Development Ai2sd 2024 written by Mostafa Ezziyyani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-19 with Computers categories.
This book provides a dynamic platform for exploring groundbreaking advancements in intelligent systems for sustainable development. It offers readers’ access to the latest technologies and innovative solutions that address global challenges. Bringing together leading academics, pioneering researchers, and industry leaders fosters knowledge exchange across various fields such as health, education, agriculture, energy, and security. It enables readers to gain valuable insights, build strategic partnerships, and contribute to shaping a more sustainable future. This book bridges scientific research with practical applications and is ideal for researchers, practitioners, and decision-makers, driving progress across multiple disciplines.
Quantum Computing
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Author : Shrikant Tiwari
language : en
Publisher: CRC Press
Release Date : 2025-04-17
Quantum Computing written by Shrikant Tiwari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-17 with Computers categories.
Quantum computing and algorithms are set to revolutionize information processing. Covering such topics, Quantum Computing: The Future of Information Processing explains its principles, practical applications, and future implications in a clear and accessible manner. The book strives to simplify the essential concepts and practical applications of quantum computing. Its aim is to help students and researchers to apply quantum computing to advance AI and machine learning, cybersecurity, and blockchain. With its emphasis on practical applications, the book covers how quantum computing is changing such fields as: Finance Medicine Built environment Networking and communications With extensive real-world case studies and practical implementation guidance, the book is a guide for those seeking to understand how quantum computing is applied in various industries. Its in-depth exploration of quantum computing covers both foundational principles and advanced applications in a single resource, saving readers the need to purchase multiple books. Finally, the book focuses on the future of information processing so that students and researchers can anticipate and prepare for the transformative impact of quantum computing.
Is My Phone Reading My Mind
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Author : Matt Agnew
language : en
Publisher: Allen & Unwin
Release Date : 2024-07-30
Is My Phone Reading My Mind written by Matt Agnew and has been published by Allen & Unwin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Juvenile Nonfiction categories.
Let Dr Matt explain everything you and your kids need to know about Artificial Intelligence and why you don't need to be afraid! When you think of AI, you might imagine a walking, talking robot or you might think of a giant computer that wants to take over the world, but the reality is that AI is a brilliant human invention that can be found in nearly every modern device from our computers to our cars. AI can seem scary at times, so working out where we use AI and why is an important part of making the best of this exciting technology. So, what is an algorithm and can it help you choose pizza? Can ChatGPT do your homework? And when you watch TV, is it watching you back? All these questions and more about AI are answered in a fun, funny and engaging way. Dr Matt Agnew has a Doctorate in Astrophysics and a Masters in Artificial Intelligence, and believes in making STEM accessible for everyone.