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| federated learning: strategies for improving communication efficiency: Communication Efficient Federated Learning for Wireless Networks Mingzhe Chen, |
| federated learning: strategies for improving communication efficiency: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 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: strategies for improving communication efficiency: ECAI 2020 G. De Giacomo, A. Catala, B. Dilkina, 2020-09-11 This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology. |
| federated learning: strategies for improving communication efficiency: Federated Learning Jayakrushna Sahoo, Mariya Ouaissa, Akarsh K. Nair, 2024-09-20 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: strategies for improving communication efficiency: Federated Learning Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen, 2022-11-29 This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. |
| federated learning: strategies for improving communication efficiency: Federated Learning Heiko Ludwig, Nathalie Baracaldo, 2022-07-07 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: strategies for improving communication efficiency: Federated Learning Systems Muhammad Habib ur Rehman, Mohamed Medhat Gaber, 2021-06-11 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: strategies for improving communication efficiency: Federated Learning Techniques And Its Application In The Healthcare Industry H L Gururaj, Tanuja Kayarga, Francesco Flammini, Dalibor Dobrilovic, 2024-05-28 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. |
| federated learning: strategies for improving communication efficiency: Handbook of Trustworthy Federated Learning My T. Thai, |
| federated learning: strategies for improving communication efficiency: Handbook on Federated Learning Saravanan Krishnan, A. Jose Anand, R. Srinivasan, R. Kavitha, S. Suresh, 2024-01-09 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. |
| federated learning: strategies for improving communication efficiency: Model Optimization Methods for Efficient and Edge AI Pethuru Raj Chelliah, Amir Masoud Rahmani, Robert Colby, Gayathri Nagasubramanian, Sunku Ranganath, 2024-11-13 Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders. |
| federated learning: strategies for improving communication efficiency: Federated Learning for Smart Communication using IoT Application Kaushal Kishor, Parma Nand, Vishal Jain, Neetesh Saxena, Gaurav Agarwal, Rani Astya, 2024-10-30 The effectiveness of federated learning in high‐performance information systems and informatics‐based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‐based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications. Features: • Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy. • Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy. • Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area. • Analyses the need for a personalized federated learning framework in cloud‐edge and wireless‐edge architecture for intelligent IoT applications. • Comprises real‐life case illustrations and examples to help consolidate understanding of topics presented in each chapter. This book is recommended for anyone interested in federated learning‐based intelligent algorithms for smart communications. |
| federated learning: strategies for improving communication efficiency: Wireless Algorithms, Systems, and Applications Lei Wang, Michael Segal, Jenhui Chen, Tie Qiu, 2022-11-17 The three-volume set constitutes the proceedings of the 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022, which was held during November 24th-26th, 2022. The conference took place in Dalian, China.The 95 full and 62 short papers presented in these proceedings were carefully reviewed and selected from 265 submissions. The contributions in cyber-physical systems including intelligent transportation systems and smart healthcare systems; security and privacy; topology control and coverage; energy-efficient algorithms, systems and protocol design |
| federated learning: strategies for improving communication efficiency: Digital Forensics and Cyber Crime Sanjay Goel, Pavel Gladyshev, Akatyev Nikolay, George Markowsky, Daryl Johnson, 2023-07-15 This book constitutes the refereed proceedings of the 13th EAI International Conference on Practical Aspects of Digital Forensics and Cyber Crime, ICDF2C 2022, held in Boston, MA, during November 16-18, 2022. The 28 full papers included in this book were carefully reviewed and selected from 80 submissions. They were organized in topical sections as follows: Image Forensics; Forensics Analysis; spread spectrum analysis; traffic analysis and monitoring; malware analysis; security risk management; privacy and security. |
| federated learning: strategies for improving communication efficiency: Smart Trends in Computing and Communications Yu-Dong Zhang, Tomonobu Senjyu, Chakchai So-In, Amit Joshi, 2021-10-25 This book gathers high-quality papers presented at the Fifth International Conference on Smart Trends in Computing and Communications (SmartCom 2021), organized by Global Knowledge Research Foundation (GR Foundation) from March 2 – 3 , 2021. It covers the state of the art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications. |
| federated learning: strategies for improving communication efficiency: Blockchain and Trustworthy Systems Zibin Zheng, Hong-Ning Dai, Xiaodong Fu, Benhui Chen, 2020-11-11 This book constitutes the thoroughly refereed post conference papers of the Second International Conference on Blockchain and Trustworthy Systems, Blocksys 2020, held in Dali, China*, in August 2020. The 42 full papers and the 11 short papers were carefully reviewed and selected from 100 submissions. The papers are organized in topical sections: theories and algorithms for blockchain, performance optimization of blockchain, blockchain security and privacy, blockchain and cloud computing, blockchain and internet of things, blockchain and mobile edge computing, blockchain and smart contracts, blockchain and data mining, blockchain services and applications, trustworthy system development. *The conference was held virtually due to the COVID-19 pandemic. |
| federated learning: strategies for improving communication efficiency: Shaping the Future of IoT with Edge Intelligence Rute C. Sofia, John Soldatos, 2024-01-08 This book presents the technologies that empower edge intelligence, along with their use in novel IoT solutions. Specifically, it presents how 5G/6G, Edge AI, and Blockchain solutions enable novel IoT-based decentralized intelligence use cases at the edge of the cloud/edge/IoT continuum. Emphasis is placed on presenting how these technologies support a wide array of functional and non-functional requirements spanning latency, performance, cybersecurity, data protection, real-time performance, energy efficiency, and more. The various chapters of the book are contributed by several EU-funded projects, which have recently developed novel IoT platforms that enable the development and deployment of edge intelligence applications based on the cloud/edge paradigm. Each one of the projects employs its own approach and uses a different mix of networking, middleware, and IoT technologies. Therefore, each of the chapters of the book contributes a unique perspective on the capabilities of enabling technologies and their integration in practical real-life applications in different sectors. The book is structured in five distinct parts. Each one of the first four parts focuses on a specific set of enabling technologies for edge intelligence and smart IoT applications in the cloud/edge/IoT continuum. Furthermore, the fifth part provides information about complementary aspects of next-generation IoT technology, including information about business models and IoT skills. Specifically: The first part focuses on 5G/6G networking technologies and their roles in implementing edge intelligence applications. The second part presents IoT applications that employ machine learning and other forms of Artificial Intelligence at the edge of the network. The third part illustrates decentralized IoT applications based on distributed ledger technologies. The fourth part is devoted to the presentation of novel IoT applications and use cases spanning the cloud/edge/IoT continuum. The fifth part discusses complementary aspects of IoT technologies, including business models and digital skills. |
| federated learning: strategies for improving communication efficiency: Smart Grid and Internet of Things Der-Jiunn Deng, |
| federated learning: strategies for improving communication efficiency: Implementing Industry 4.0 Carlos Toro, Wei Wang, Humza Akhtar, 2021-04-03 This book relates research being implemented in three main research areas: secure connectivity and intelligent systems, real-time analytics and manufacturing knowledge and virtual manufacturing. Manufacturing SMEs and MNCs want to see how Industry 4.0 is implemented. On the other hand, groundbreaking research on this topic is constantly growing. For the aforesaid reason, the Singapore Agency for Science, Technology and Research (A*STAR), has created the model factory initiative. In the model factory, manufacturers, technology providers and the broader industry can (i) learn how I4.0 technologies are implemented on real-world manufacturing use-cases, (ii) test process improvements enabled by such technologies at the model factory facility, without disrupting their own operations, (iii) co-develop technology solutions and (iv) support the adoption of solutions at their everyday industrial operation. The book constitutes a clear base ground not only for inspiration of researchers, but also for companies who will want to adopt smart manufacturing approaches coming from Industry 4.0 in their pathway to digitization. |
| federated learning: strategies for improving communication efficiency: Edge Intelligent Computing Systems in Different Domains Benedetta Picano, Romano Fantacci, 2024-02-11 This book provides a comprehensive and systematic exploration of next-generation Edge Intelligence (EI) Networks. It delves deep into the critical design considerations within this context, emphasizing the necessity for functional and dependable interactions between networking strategies and the diverse application scenarios. This should help assist to encompass a wide range of environments. This book also discusses topics such as resource optimization, incentive mechanisms, channel prediction and cutting-edge technologies, which includes digital twins and advanced machine learning techniques. It underscores the importance of functional integration to facilitate meaningful collaborations between networks and systems, while operating across heterogeneous environments aiming support novel and disruptive human-oriented services and applications. Valuable insights into the stringent requirements for intelligence capabilities, communication latency and real-time response are discussed. This characterizes the new EI era, driving the creation of comprehensive cross-domain architectural ecosystems that infuse human-like intelligence into every aspect of emerging EI systems. This book primarily targets advanced-level students as well as postdoctoral researchers, who are new to this field and are searching for a comprehensive understanding of emerging EI systems. Practitioners seeking guidance in the development and implementation of EI systems in practical contexts will also benefit from this book. |
| federated learning: strategies for improving communication efficiency: Advances in Intelligent Data Analysis XIX Pedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama, 2021-04-12 This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021. The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats. |
| federated learning: strategies for improving communication efficiency: Computer Security – ESORICS 2020 Liqun Chen, Ninghui Li, Kaitai Liang, Steve Schneider, 2020-09-11 The two volume set, LNCS 12308 + 12309, constitutes the proceedings of the 25th European Symposium on Research in Computer Security, ESORICS 2020, which was held in September 2020. The conference was planned to take place in Guildford, UK. Due to the COVID-19 pandemic, the conference changed to an online format. The total of 72 full papers included in these proceedings was carefully reviewed and selected from 366 submissions. The papers were organized in topical sections named: database and Web security; system security; network security; software security; machine learning security; privacy; formal modelling; applied cryptography; analyzing attacks; post-quantum cryptogrphy; security analysis; and blockchain. |
| federated learning: strategies for improving communication efficiency: Privacy Preservation in IoT: Machine Learning Approaches Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang, 2022-04-27 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: strategies for improving communication efficiency: Software Architecture Stefan Biffl, Elena Navarro, Welf Löwe, Marjan Sirjani, Raffaela Mirandola, Danny Weyns, 2021-08-25 This book constitutes the refereed proceedings of the 15th International Conference on Software Architecture, ECSA 2021, held in Sweden, in September 2021. Due to the COVID-19 pandemic, the conference was held virtually. For the Research Track, 11 full papers, presented together with 5 short papers, were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections as follows: architectures for reconfigurable and self-adaptive systems; machine learning for software architecture; architectural knowledge, decisions, and rationale; architecting for quality attributes; architecture-centric source code analysis; and experiences and learnings from industrial case studies. |
| federated learning: strategies for improving communication efficiency: Simulation and Synthesis in Medical Imaging Can Zhao, David Svoboda, Jelmer M. Wolterink, Maria Escobar, 2022-09-21 This book constitutes the refereed proceedings of the 7th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2022, held in conjunction with MICCAI 2022, in Singapore, Singapore in September 2022. |
| federated learning: strategies for improving communication efficiency: Privacy-Preserving in Edge Computing Longxiang Gao, Tom H. Luan, Bruce Gu, Youyang Qu, Yong Xiang, 2021-06-01 With the rapid development of big data, it is necessary to transfer the massive data generated by end devices to the cloud under the traditional cloud computing model. However, the delays caused by massive data transmission no longer meet the requirements of various real-time mobile services. Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm. By extending the functions of the cloud to the edge of the network, edge computing provides effective data access control, computation, processing and storage for end devices. Furthermore, edge computing optimizes the seamless connection from the cloud to devices, which is considered the foundation for realizing the interconnection of everything. However, due to the open features of edge computing, such as content awareness, real-time computing and parallel processing, the existing problems of privacy in the edge computing environment have become more prominent. The access to multiple categories and large numbers of devices in edge computing also creates new privacy issues. In this book, we discuss on the research background and current research process of privacy protection in edge computing. In the first chapter, the state-of-the-art research of edge computing are reviewed. The second chapter discusses the data privacy issue and attack models in edge computing. Three categories of privacy preserving schemes will be further introduced in the following chapters. Chapter three introduces the context-aware privacy preserving scheme. Chapter four further introduces a location-aware differential privacy preserving scheme. Chapter five presents a new blockchain based decentralized privacy preserving in edge computing. Chapter six summarize this monograph and propose future research directions. In summary, this book introduces the following techniques in edge computing: 1) describe an MDP-based privacy-preserving model to solve context-aware data privacy in the hierarchical edge computing paradigm; 2) describe a SDN based clustering methods to solve the location-aware privacy problems in edge computing; 3) describe a novel blockchain based decentralized privacy-preserving scheme in edge computing. These techniques enable the rapid development of privacy-preserving in edge computing. |
| federated learning: strategies for improving communication efficiency: Advances in Computer Science and Ubiquitous Computing Ji Su Park, Laurence T. Yang, Yi Pan, Jong Hyuk Park, 2023-07-04 This book presents the combined proceedings of the 14th International Conference on Computer Science and its Applications (CSA 2022) and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2022), both held in Vientiane, Laos, December 19-21, 2022. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies & Computer Science and its Applications. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science and other disciplines related to ubiquitous computing. |
| federated learning: strategies for improving communication efficiency: Frontiers in Cyber Security Chunjie Cao, Yuqing Zhang, Yuan Hong, Ding Wang, 2022-02-28 This volume constitutes the proceedings of the 4th International Conference on Frontiers in Cyber Security, FCS 2021, held in Haikou, China, in December 2021. The 20 full papers along with the 2 short papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on: intelligent security; system security; network security; multimedia security; privacy, risk and trust; data and application security. |
| federated learning: strategies for improving communication efficiency: Advances in Service-Oriented and Cloud Computing Christian Zirpins, Guadalupe Ortiz, Zoltan Nochta, Oliver Waldhorst, Jacopo Soldani, Massimo Villari, Damian Tamburri, 2023-01-01 This volume contains the technical papers presented in the workshops, which took place at the 9th European Conference on Service-Oriented and Cloud Computing, ESOCC 2022, held in Wittenberg, Germany, in March 2022. The 4 full papers and 7 short papers included in these proceedings were carefully reviewed and selected from 17 submissions. The workshop proceedings volume of ESOCC 2022 contains contributions from the following workshops and events: First International Workshop on AI for Web Application Infrastructure and Cloud Platform Security (AWACS 2022)PhD Symposium of ESOCC 2022ESOCC 2022 Projects TrackESOCC 2022 Industrial Track |
| federated learning: strategies for improving communication efficiency: Machine Learning for Cyber Security Dan Dongseong Kim, |
| federated learning: strategies for improving communication efficiency: Social Edge Computing Dong Wang, Daniel 'Yue' Zhang, 2023-06-22 The rise of the Internet of Things (IoT) and Artificial Intelligence (AI) leads to the emergence of edge computing systems that push the training and deployment of AI models to the edge of networks for reduced bandwidth cost, improved responsiveness, and better privacy protection, allowing for the ubiquitous AI that can happen anywhere and anytime. Motivated by the above trend, this book introduces a new computing paradigm, the Social Edge Computing (SEC), that empowers human-centric edge intelligent applications by revolutionizing the computing, intelligence, and the training of the AI models at the edge. The SEC paradigm introduces a set of critical human-centric challenges such as the rational nature of edge device owners, pronounced heterogeneity of the edge devices, real-time AI at the edge, human and AI interaction, and the privacy of the edge users. The book addresses these challenges by presenting a series of principled models and systems that enable the confluence of the computing capabilities of devices and the domain knowledge of the people, while explicitly addressing the unique concerns and constraints from humans. Compared to existing books in the field of edge computing, the vision of this book is unique: we focus on the social edge computing (SEC), an emerging paradigm at the intersection of edge computing, AI, and social computing. This book discusses the unique vision, challenges and applications in SEC. To our knowledge, keeping humans in the loop of edge intelligence has not been systematically reviewed and studied in an existing book. The SEC vision generalizes the current machine-to-machine interactions in edge computing (e.g., mobile edge computing literature), and machine-to-AI interactions (e.g., edge intelligence literature) into a holistic human-machine-AI ecosystem. |
| federated learning: strategies for improving communication efficiency: Deep Learning for Robot Perception and Cognition Alexandros Iosifidis, Anastasios Tefas, 2022-02-04 Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis |
| federated learning: strategies for improving communication efficiency: Euro-Par 2024: Parallel Processing Jesus Carretero, |
| federated learning: strategies for improving communication efficiency: Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Nur Zincir-Heywood, Marco Mellia, Yixin Diao, 2021-10-12 COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management. |
| federated learning: strategies for improving communication efficiency: Simulation Tools and Techniques Houbing Song, Dingde Jiang, 2021-04-26 This two-volume set constitutes the refereed post-conference proceedings of the 12th International Conference on Simulation Tools and Techniques, SIMUTools 2020, held in Guiyang, China, in August 2020. Due to COVID-19 pandemic the conference was held virtually. The 125 revised full papers were carefully selected from 354 submissions. The papers focus on simulation methods, simulation techniques, simulation software, simulation performance, modeling formalisms, simulation verification and widely used frameworks. |
| federated learning: strategies for improving communication efficiency: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries Alessandro Crimi, Spyridon Bakas, 2022-07-21 This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book. |
| federated learning: strategies for improving communication efficiency: Advances in Data-driven Computing and Intelligent Systems Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal, 2023-06-21 The volume is a collection of best selected research papers presented at International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022) held at BITS Pilani, K K Birla Goa Campus, Goa, India during 23 – 25 September 2022. It includes state-of-the art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book will be useful for academicians, research scholars, and industry persons. |
| federated learning: strategies for improving communication efficiency: Meta Learning With Medical Imaging and Health Informatics Applications Hien Van Nguyen, Ronald Summers, Rama Chellappa, 2022-09-24 Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks' fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. - First book on applying Meta Learning to medical imaging - Pioneers in the field as contributing authors to explain the theory and its development - Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly |
| federated learning: strategies for improving communication efficiency: Data Fusion Techniques and Applications for Smart Healthcare Amit Kumar Singh, Stefano Berretti, 2024-03-12 Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, x-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. These large and highly diverse amounts of information need to be organized and mined in an appropriate way so that meaningful information can be extracted. New multimodal data fusion techniques are able to combine salient information into one single source to ensure better diagnostic accuracy and assessment. Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. This book can be used as a reference for practicing engineers, scientists, and researchers. It will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications. - Presents broad coverage of applied case studies using data fusion techniques to mine, organize, and interpret medical data - Investigates how data fusion techniques offer a new solution for dealing with massive amounts of medical data coming from diverse sources and multiple formats - Focuses on identifying challenges, solutions, and new directions that will be useful for graduate students, researchers, and practitioners from government, academia, industry, and healthcare |
| federated learning: strategies for improving communication efficiency: MultiMedia Modeling Stevan Rudinac, |
Federated Hermes: leader in active asset management
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Federated Hermes: leader in active asset management
06-04-2025 June 4 2025 Juneteenth National Independence Day Holiday Observance 2025 . Thursday, June 19, 2025: Federated Hermes and the New York Stock Exchange (NYSE) will …
About Us - Federated Hermes
Jun 2, 2025 · Federated Hermes, Inc. (NYSE: FHI) is a leading global asset manager focused on meeting the diverse and evolving needs of today's investors. Guided by our conviction that …
Prime Cash Obligations Fund - Federated Hermes
Jun 2, 2025 · Seeks current income consistent with stability of principal and liquidity by investing primarily in a portfolio of high-quality, dollar-denominated, fixed-income securities that: (1) are …
Insights - Federated Hermes
Jun 2, 2025 · Guided by our conviction that responsible investing is the best way to create wealth over the long term, Federated Hermes offers investment solutions across a range of asset …
Institutional Prime Obligations Fund - Federated Hermes
Jun 2, 2025 · Seeks current income consistent with stability of principal by investing primarily in a portfolio of high-quality, dollar denominated fixed-income securities that: (1) are issued by …
Government Obligations Tax-Managed Fund - Federated Hermes
Jun 2, 2025 · Pursues current income consistent with stability of principal and liquidity by investing in a portfolio of U.S. Treasury and government securities maturing in 397 days or less that pay …
Open and Manage Your Account - Federated Hermes
Jun 2, 2025 · Learn how to open an account, designate beneficiaries and intermediaries, direct income, set up banking, systematic options, e-delivery and more.
Ultrashort Bond Fund (FULIX) - Federated Hermes
Jun 2, 2025 · Prior to October 27, 1998, the fund was named Federated Limited Duration Government Fund with an investment concentration in government securities. The fund was …
Our History - Federated Hermes
Jun 2, 2025 · Federated negotiated with the U.S. Treasury Department to keep exchange funds tax-free. By allowing investors to "exchange" their shares of common stocks for shares of the …
Corporate Overview - Federated Hermes
Jun 2, 2025 · 1 Formerly Federated Investors, Inc. (FII), prior to its Q1 2020 name and ticker symbol change.. Separately managed accounts are made available through Federated Global …
