Federated Learning For Internet Of Vehicles
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Federated Learning Based Intelligent Systems To Handle Issues And Challenges In Iovs Part 1
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Author : Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav
language : en
Publisher: Bentham Science Publishers
Release Date : 2024-12-13
Federated Learning Based Intelligent Systems To Handle Issues And Challenges In Iovs Part 1 written by Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.
Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1) examines how federated learning can address key challenges within the Internet of Vehicles, from data security to routing efficiency. This volume explores how federated learning, a decentralized approach to machine learning, enables secure and adaptive IoV systems that enhance road safety, optimize traffic flow, and support reliable data sharing. Chapters cover essential topics, including technologies to address IoV routing issues, secure data exchange using blockchain, privacy-preserving methods, and NLP applications for vehicle safety. By combining theoretical insights with practical solutions, the book highlights how federated learning fosters scalable, resilient IoV systems that respond dynamically to the demands of connected vehicles. Key Features: - Addresses data privacy, secure communication, and adaptive solutions in IoV - Explores federated learning applications in real-time IoV systems - Combines practical examples with theoretical foundations in IoV technology - Includes emerging research areas in IoV federated learning frameworks
Internet Of Vehicles And Computer Vision Solutions For Smart City Transformations
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Author : Anuj Abraham
language : en
Publisher: Springer Nature
Release Date : 2025-02-19
Internet Of Vehicles And Computer Vision Solutions For Smart City Transformations written by Anuj Abraham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-19 with Technology & Engineering categories.
This book compiles recent research endeavors at the intersection of computer vision (CV) and deep learning for Internet of Vehicles (IoV) applications, which are pivotal in shaping the landscape of smart cities. These technologies play instrumental roles in enhancing various facets of urban life, encompassing safety, transportation, infrastructure management, and sustainability. The amalgamation of CV and deep learning within smart cities creates a powerful synergy that fosters safer, more efficient, and sustainable urban environments. By harnessing these cutting-edge technologies to drive data-driven decision-making, cities can elevate the quality of life for their inhabitants, mitigate environmental impact, and optimize overall urban functionality. Additionally, this compilation provides in-depth technical and scientific insights into various facets of artificial intelligence (AI) technologies, including forthcoming trends and innovations that are poised to transform smart cities. The book also extends its focus to other areas of smart city development. It explores the application of these technologies in the creation of smart parking solutions, discusses the role of surveillance for public safety, and examines how CV and IoV can be utilized for environmental monitoring. The book also delves into urban planning and infrastructure development, emphasizing the importance of a data-driven approach. It sheds light on the social impact of smart cities and the importance of citizen engagement and discusses issues of security and privacy in the context of smart cities. The book concludes with a look at future trends and challenges in the field of smart cities. Targeted at researchers, practitioners, engineers, and scientists, this book is geared toward those engaged in the development of advanced algorithms for future-forward smart city applications in computer vision, vehicular networking, communication technology, sensor devices, IoT communication, vehicular and on-road safety, data security, and services for IoV-related devices.
Federated Learning For Internet Of Vehicles
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Author : Shelly Gupta
language : en
Publisher:
Release Date : 2024
Federated Learning For Internet Of Vehicles written by Shelly Gupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Artificial intelligence categories.
Federated Learning Based Intelligent Systems To Handle Issues And Challenges In Iovs Part 2
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Author : Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav
language : en
Publisher: Bentham Science Publishers
Release Date : 2025-04-25
Federated Learning Based Intelligent Systems To Handle Issues And Challenges In Iovs Part 2 written by Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-25 with Computers categories.
Federated Learning for Internet of Vehicles: IoV Image Processing, Vision, and Intelligent Systems (Volume 3) explores how federated learning is revolutionizing the Internet of Vehicles (IoV) by enabling secure, decentralized, and scalable solutions. Combining theoretical insights with practical applications, this book addresses key challenges such as data privacy, heterogeneous information, and network latency in IoV systems. This volume offers cutting-edge strategies to build intelligent, resilient vehicular systems, from privacy-enhanced data collection to blockchain-based payments, smart transportation systems, and vehicle number plate recognition. It highlights how federated learning drives advancements in secure data sharing, identity-based authentication, and real-time road safety improvements. Key Features: - In-depth exploration of federated learning applications in IoV. - Solutions for privacy, security, and scalability challenges. - Practical examples of blockchain integration and smart systems. - Insights into future research directions for IoV.
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.”
Federated Learning For Internet Of Vehicles
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Author : Adarsh Garg
language : en
Publisher:
Release Date : 2023
Federated Learning For Internet Of Vehicles written by Adarsh Garg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Artificial intelligence categories.
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.
Handbook On Federated Learning
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Author : Saravanan Krishnan
language : en
Publisher: CRC Press
Release Date : 2024-01-09
Handbook On Federated Learning written by Saravanan Krishnan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
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.
Intelligent Technologies For Internet Of Vehicles
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Author : Naercio Magaia
language : en
Publisher: Springer Nature
Release Date : 2021-06-09
Intelligent Technologies For Internet Of Vehicles written by Naercio Magaia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-09 with Technology & Engineering categories.
This book gathers recent research works in emerging Artificial Intelligence (AI) methods for the convergence of communication, caching, control, and computing resources in cloud-based Internet of Vehicles (IoV) infrastructures. In this context, the book's major subjects cover the analysis and the development of AI-powered mechanisms in future IoV applications and architectures. It addresses the major new technological developments in the field and reflects current research trends and industry needs. It comprises a good balance between theoretical and practical issues, covering case studies, experience and evaluation reports, and best practices in utilizing AI applications in IoV networks. It also provides technical/scientific information about various aspects of AI technologies, ranging from basic concepts to research-grade material, including future directions. This book is intended for researchers, practitioners, engineers, and scientists involved in designing and developing protocols and AI applications and services for IoV-related devices.
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.