Federated Learning For Internet Of Vehicles
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| Author | : Satya Prakash Yadav |
| Publisher | : Springer Nature |
| Total Pages | : 269 |
| Release | : 2022-02-02 |
| Genre | : Technology & Engineering |
| ISBN | : 3030855597 |
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.
| Author | : Adarsh Garg |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : Artificial intelligence |
| ISBN | : 9789815165807 |
| Author | : Wenchao Xu |
| Publisher | : Springer Nature |
| Total Pages | : 175 |
| Release | : 2021-11-18 |
| Genre | : Computers |
| ISBN | : 3030889912 |
This book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. To enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. In addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme. This book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. Researchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book.
| Author | : Wenchao Xu |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : 9783030889920 |
This book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. To enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. In addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme. This book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. Researchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book. .
| Author | : Choong Seon Hong |
| Publisher | : Springer Nature |
| Total Pages | : 257 |
| Release | : 2022-01-01 |
| Genre | : Computers |
| ISBN | : 9811649634 |
Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
| Author | : Qiang Yang |
| Publisher | : Springer Nature |
| Total Pages | : 291 |
| Release | : 2020-11-25 |
| Genre | : Computers |
| ISBN | : 3030630765 |
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.”
| Author | : Roozbeh Razavi-Far |
| Publisher | : Springer Nature |
| Total Pages | : 371 |
| Release | : 2022-09-30 |
| Genre | : Technology & Engineering |
| ISBN | : 3031117484 |
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.
| Author | : Saravanan Krishnan |
| Publisher | : CRC Press |
| Total Pages | : 381 |
| Release | : 2024-01-09 |
| Genre | : Computers |
| ISBN | : 1003837522 |
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.
| Author | : Pawan Subedi |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : Electronic dissertations |
| ISBN | : |
Autonomous driving relies greatly on deep learning models to comprehend the surroundings and activities of the road systems. These learning models are traditionally trained off-line and used during driving. However, recent research on federated learning has enabled distributed deep learning for model adaptation with new data inputs from end users. Similarly, the recent research on incremental learning has enabled the upgrading of the learned model with newer rounds of training, without losing previously acquired knowledge. For autonomous and/or connected vehicles (AVs, CVs), these mean it is possible to take new, maybe real time, data inputs from various sensors from multiple vehicles in vicinity, to train and update the preloaded models. The updated models can improve the safety and reduce human involvements when driving through unfamiliar situations. Despite the tremendous possibilities of federated learning for autonomous driving, it faces several challenges for its implementation. The challenges include the heterogeneity of the end devices, statistical heterogeneity of the available training data, emerging privacy challenges, energy management, model data distribution, etc. Although recent works in the literature attempt to answer these challenges, the assumption of identical distribution of the training data in most of these solutions is difficult to achieve. Similarly, all these works lack the consideration of the challenges for distribution of the model for training in dynamic and mobile edge wireless environment.To this end, this thesis presents a networked system using Fog and Edge architecture utilizing VANET and VDTN for model training management answering the need for distribution of the model. A novel scheme for federated learning utilizing the networked systems is then presented. The presented scheme for federated learning has quicker convergence and also considers the presence of data with varied distribution at the end devices. Finally, to answer the need for reliable data dissemination in case of lack of VANET and VDTN based communication, a complimentary network system utilizing the ultra dense network is presented.
| Author | : Naercio Magaia |
| Publisher | : Springer Nature |
| Total Pages | : 511 |
| Release | : 2021-06-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3030764931 |
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.
