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Tag: Federated

  • Federated Learning in the Age of Foundation Models – FL 2024 International Workshops: FL@FM-WWW 2024, Singapore, May 14, 2024; FL@FM-ICME 2024, … (Lecture Notes in Computer Science, 15501)

    Federated Learning in the Age of Foundation Models – FL 2024 International Workshops: FL@FM-WWW 2024, Singapore, May 14, 2024; FL@FM-ICME 2024, … (Lecture Notes in Computer Science, 15501)


    Price: $54.99 – $52.24
    (as of Dec 25,2024 01:09:42 UTC – Details)




    Publisher ‏ : ‎ Springer (February 18, 2025)
    Language ‏ : ‎ English
    ISBN-10 ‏ : ‎ 3031822390
    ISBN-13 ‏ : ‎ 978-3031822391
    Item Weight ‏ : ‎ 1.11 pounds


    Federated Learning in the Age of Foundation Models – FL 2024 International Workshops: FL@FM-WWW 2024, Singapore, May 14, 2024; FL@FM-ICME 2024

    The field of federated learning is rapidly evolving, especially with the emergence of foundation models that have revolutionized the way we approach machine learning tasks. As we look towards the future, it is crucial to stay updated on the latest developments and trends in federated learning.

    The FL 2024 International Workshops, including FL@FM-WWW 2024 and FL@FM-ICME 2024, are set to take place in Singapore on May 14, 2024. These workshops will bring together experts, researchers, and practitioners in the field of federated learning to discuss and exchange ideas on the latest advancements in the field.

    Topics that will be covered include but are not limited to:
    – Advances in federated learning algorithms
    – Privacy-preserving techniques in federated learning
    – Scalability and efficiency in federated learning
    – Federated learning in edge computing environments
    – Applications of federated learning in various industries

    The workshops will also feature keynote speakers, panel discussions, and paper presentations on cutting-edge research in federated learning. Participants will have the opportunity to network with peers, collaborate on research projects, and gain valuable insights into the future of federated learning.

    For more information on the FL 2024 International Workshops and to register for the event, please visit the Lecture Notes in Computer Science, 15501 website. Don’t miss this opportunity to be at the forefront of federated learning in the age of foundation models!
    #Federated #Learning #Age #Foundation #Models #International #Workshops #FLFMWWW #Singapore #FLFMICME #Lecture #Notes #Computer #Science

  • Integration of Federated Learning and Blockchain for Smart Cities

    Integration of Federated Learning and Blockchain for Smart Cities


    Price: $225.00
    (as of Dec 25,2024 00:27:41 UTC – Details)




    Publisher ‏ : ‎ Wiley-Scrivener; 1st edition (August 26, 2025)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 400 pages
    ISBN-10 ‏ : ‎ 1394166451
    ISBN-13 ‏ : ‎ 978-1394166459
    Item Weight ‏ : ‎ 1.47 pounds


    Federated learning and blockchain are two cutting-edge technologies that have the potential to revolutionize the way smart cities operate. Federated learning allows multiple parties to collaborate on a machine learning model without sharing their data, while blockchain provides a secure and transparent way to store and verify information. By integrating these two technologies, smart cities can enhance data privacy, security, and efficiency.

    One of the key benefits of integrating federated learning and blockchain in smart cities is the enhanced data privacy and security. With federated learning, data remains on the devices of individual users or organizations, and only aggregated model updates are shared between parties. This helps protect sensitive information and reduce the risk of data breaches. By storing these model updates on a blockchain, smart cities can ensure the integrity and immutability of the data, further enhancing security.

    Another advantage of this integration is improved efficiency in data processing and analysis. Federated learning allows for distributed computation, enabling multiple devices to collaboratively train machine learning models. By leveraging blockchain technology to securely store and verify these model updates, smart cities can streamline the process of collecting, analyzing, and acting on data. This can lead to faster decision-making and more accurate insights for urban planning, resource allocation, and other city operations.

    Furthermore, the combination of federated learning and blockchain can enable new decentralized applications and services in smart cities. For example, smart contracts on a blockchain could automate and enforce agreements between parties participating in federated learning collaborations. This could facilitate the sharing of resources, rewards, and governance mechanisms in a transparent and trustless manner.

    In conclusion, the integration of federated learning and blockchain holds great promise for enhancing the efficiency, privacy, and security of smart cities. By leveraging these technologies together, cities can unlock new opportunities for collaborative data analysis, decentralized decision-making, and innovative services. As smart cities continue to evolve and grow, the fusion of federated learning and blockchain will play a pivotal role in shaping the future of urban living.
    #Integration #Federated #Learning #Blockchain #Smart #Cities

  • Learning Together Science for Kids: Explore the concept of federated learning! (Book for Curious Kids)

    Learning Together Science for Kids: Explore the concept of federated learning! (Book for Curious Kids)


    Price: $11.95
    (as of Dec 24,2024 22:27:16 UTC – Details)



    Are you looking for a fun and engaging way to introduce your child to the fascinating world of science? Look no further than “Learning Together Science for Kids: Explore the concept of federated learning!” This book is perfect for curious kids who want to learn more about cutting-edge technologies and how they work.

    In this book, children will be introduced to the concept of federated learning, a revolutionary approach to machine learning that allows multiple devices to collaborate and learn from each other without sharing sensitive data. Through colorful illustrations and easy-to-understand explanations, young readers will discover how federated learning is used in various applications, such as personalized recommendations and predictive modeling.

    By exploring the concept of federated learning, children will develop a deeper understanding of how technology shapes our world and how collaboration can lead to innovative solutions. So why wait? Dive into the world of federated learning with your child today and spark their curiosity for science and technology!
    #Learning #Science #Kids #Explore #concept #federated #learning #Book #Curious #Kids

  • Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning (Karlsruher Schriftenreihe Fahrzeugsystemtechnik)

    Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning (Karlsruher Schriftenreihe Fahrzeugsystemtechnik)


    Price: $69.00
    (as of Dec 24,2024 21:05:59 UTC – Details)




    Publisher ‏ : ‎ KIT Scientific Publishing (September 3, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 192 pages
    ISBN-10 ‏ : ‎ 3731513714
    ISBN-13 ‏ : ‎ 978-3731513711
    Item Weight ‏ : ‎ 13.1 ounces
    Dimensions ‏ : ‎ 5.83 x 0.44 x 8.27 inches


    In a new study published in the Karlsruher Schriftenreihe Fahrzeugsystemtechnik, researchers have developed a novel approach for predicting energy demand and driving range for electric vehicles using federated learning.

    Federated learning is a machine learning technique that allows multiple parties to collaborate on a shared model without sharing their raw data. This approach is particularly useful for predicting energy demand and driving range for electric vehicles, as it allows for the aggregation of data from various sources while maintaining privacy and security.

    The researchers used a probabilistic prediction model to estimate energy demand and driving range for electric vehicles based on factors such as weather conditions, traffic patterns, and driving behavior. By leveraging federated learning, the model was able to continuously learn and improve from data collected from different sources, leading to more accurate predictions.

    This study represents a significant advancement in the field of electric vehicle technology, as accurate predictions of energy demand and driving range are essential for optimizing vehicle performance and improving user experience. The researchers hope that their findings will help accelerate the adoption of electric vehicles and contribute to a more sustainable transportation system.
    #Probabilistic #Prediction #Energy #Demand #Driving #Range #Electric #Vehicles #Federated #Learning #Karlsruher #Schriftenreihe #Fahrzeugsystemtechnik

  • Federated Learning for Multimedia Data Processing and Security in Industry 5.0 (Computing and Networks)

    Federated Learning for Multimedia Data Processing and Security in Industry 5.0 (Computing and Networks)


    Price: $150.00
    (as of Dec 24,2024 20:25:06 UTC – Details)




    Publisher ‏ : ‎ The Institution of Engineering and Technology (February 1, 2025)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 334 pages
    ISBN-10 ‏ : ‎ 1839537574
    ISBN-13 ‏ : ‎ 978-1839537578
    Item Weight ‏ : ‎ 1.74 pounds


    Federated Learning for Multimedia Data Processing and Security in Industry 5.0 (Computing and Networks)

    Industry 5.0, the next phase in the evolution of manufacturing, is driven by advanced technologies such as artificial intelligence, Internet of Things (IoT), and big data analytics. In this rapidly changing landscape, multimedia data processing plays a crucial role in enabling smart factories and optimizing production processes.

    Federated learning, a decentralized machine learning approach, has emerged as a promising solution for processing multimedia data in Industry 5.0. By training models on data distributed across multiple devices or edge nodes, federated learning enables organizations to leverage the collective intelligence of their network without compromising data privacy and security.

    One of the key advantages of federated learning is its ability to process multimedia data in real-time, enabling manufacturers to make informed decisions quickly and accurately. This is particularly important in dynamic production environments where delays can result in costly downtime or inefficiencies.

    Furthermore, federated learning enhances data security by keeping sensitive information localized on individual devices, reducing the risk of data breaches or unauthorized access. This is crucial in Industry 5.0, where the interconnected nature of devices and systems increases the vulnerability of sensitive data.

    Overall, federated learning holds great potential for revolutionizing multimedia data processing in Industry 5.0, enabling manufacturers to extract valuable insights from their data while ensuring the security and privacy of their information. As the industry continues to embrace digital transformation, federated learning will play a key role in driving innovation and efficiency in manufacturing processes.
    #Federated #Learning #Multimedia #Data #Processing #Security #Industry #Computing #Networks

  • Federated AI for Real-World Business Scenarios

    Federated AI for Real-World Business Scenarios


    Price: $9.99
    (as of Dec 24,2024 19:44:43 UTC – Details)




    ASIN ‏ : ‎ B099F6VG2Q
    Publisher ‏ : ‎ CRC Press; 1st edition (September 30, 2021)
    Publication date ‏ : ‎ September 30, 2021
    Language ‏ : ‎ English
    File size ‏ : ‎ 8567 KB
    Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 218 pages
    Format ‏ : ‎ Print Replica


    Federated AI, also known as collaborative or decentralized AI, is a cutting-edge approach that allows multiple parties to collaboratively build and train machine learning models without sharing their sensitive data. This revolutionary technology holds immense potential for real-world business scenarios, offering a secure and efficient way to leverage the power of AI without compromising data privacy.

    In industries such as healthcare, finance, and manufacturing, where privacy and security are paramount, federated AI can enable organizations to extract valuable insights from their data while ensuring compliance with regulations like GDPR and HIPAA. By allowing companies to pool their data resources and collectively train machine learning models, federated AI opens up new possibilities for collaboration and innovation.

    For example, in the healthcare sector, federated AI can be used to develop predictive models for disease diagnosis and treatment recommendations by aggregating patient data from multiple hospitals and research institutions. Similarly, in finance, federated AI can help detect fraudulent transactions and assess credit risk by analyzing data from different financial institutions while protecting sensitive customer information.

    Overall, federated AI has the potential to revolutionize how businesses approach AI and machine learning, enabling them to unlock the full potential of their data assets while prioritizing data privacy and security. As this technology continues to evolve, we can expect to see even more innovative applications in a wide range of industries, driving growth, efficiency, and competitiveness in the global marketplace.
    #Federated #RealWorld #Business #Scenarios

  • Energy Optimization and Security in Federated Learning for IoT Environments (Computing and Networks)

    Energy Optimization and Security in Federated Learning for IoT Environments (Computing and Networks)


    Price: $145.00
    (as of Dec 24,2024 18:23:38 UTC – Details)




    Publisher ‏ : ‎ The Institution of Engineering and Technology (March 1, 2025)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 252 pages
    ISBN-10 ‏ : ‎ 1839539623
    ISBN-13 ‏ : ‎ 978-1839539626
    Item Weight ‏ : ‎ 1.74 pounds


    Federated learning is a decentralized approach to machine learning where multiple devices or entities collaborate to train a shared model while keeping their data local. This approach is particularly well-suited for Internet of Things (IoT) environments, where devices have limited computational power and storage capacity.

    One of the key challenges in federated learning for IoT environments is energy optimization. Since IoT devices are often battery-powered and have limited energy resources, it is important to design efficient algorithms that minimize energy consumption during the training process. This can be achieved through techniques such as model compression, quantization, and sparsification, which reduce the size and complexity of the model to be transmitted and processed on the devices.

    In addition to energy optimization, security is another critical aspect of federated learning in IoT environments. Since data is distributed across multiple devices, ensuring the privacy and confidentiality of sensitive information is paramount. Techniques such as differential privacy, secure aggregation, and homomorphic encryption can be used to protect the data and prevent unauthorized access.

    By addressing both energy optimization and security concerns in federated learning for IoT environments, we can unlock the full potential of collaborative machine learning while ensuring the efficient and secure operation of IoT devices. Researchers and practitioners in the field of computing and networks are actively working on developing innovative solutions to tackle these challenges and pave the way for the widespread adoption of federated learning in IoT applications.
    #Energy #Optimization #Security #Federated #Learning #IoT #Environments #Computing #Networks

  • Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies (Security)

    Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies (Security)


    Price: $141.40
    (as of Dec 24,2024 17:43:35 UTC – Details)




    Publisher ‏ : ‎ The Institution of Engineering and Technology (October 15, 2024)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 285 pages
    ISBN-10 ‏ : ‎ 1839539453
    ISBN-13 ‏ : ‎ 978-1839539459
    Item Weight ‏ : ‎ 1.28 pounds
    Dimensions ‏ : ‎ 6.14 x 0.69 x 9.21 inches


    Split Federated Learning for Secure IoT Applications: Concepts, Frameworks, Applications, and Case Studies (Security)

    In the era of Internet of Things (IoT) where billions of devices are connected and exchanging data, security and privacy are of utmost importance. Federated Learning has emerged as a promising technique for training machine learning models on decentralized data sources while preserving data privacy. However, traditional Federated Learning approaches still pose security risks, especially in the context of IoT applications where devices may be vulnerable to attacks.

    To address these security concerns, Split Federated Learning has been proposed as a more secure alternative. In Split Federated Learning, the model is split into multiple parts, and each part is trained on a different subset of the data. This ensures that no single entity has access to the entire model or dataset, enhancing security and privacy.

    In this post, we will delve into the concepts, frameworks, applications, and case studies of Split Federated Learning for secure IoT applications:

    1. Concepts: We will explore the underlying principles of Split Federated Learning, including how the model is split, how training is coordinated, and how the final model is aggregated.

    2. Frameworks: We will discuss existing frameworks and tools that support Split Federated Learning, such as PySyft and TensorFlow Federated, and how they can be used in IoT applications.

    3. Applications: We will showcase real-world applications of Split Federated Learning in securing IoT devices and data, such as anomaly detection, predictive maintenance, and personalized recommendations.

    4. Case Studies: We will present case studies of organizations that have successfully implemented Split Federated Learning for secure IoT applications, highlighting the benefits and challenges they faced.

    Overall, Split Federated Learning holds great promise in enhancing security and privacy in IoT applications. By understanding its concepts, leveraging frameworks, exploring applications, and learning from case studies, organizations can effectively implement this technique to protect their IoT devices and data. Stay tuned for more insights on Split Federated Learning for secure IoT applications.
    #Split #Federated #Learning #Secure #IoT #Applications #Concepts #frameworks #applications #case #studies #Security

  • Advancing Software Engineering Through AI, Federated Learning, and Large Language Models

    Advancing Software Engineering Through AI, Federated Learning, and Large Language Models


    Price: $355.00
    (as of Dec 24,2024 17:01:14 UTC – Details)



    Advancing Software Engineering Through AI, Federated Learning, and Large Language Models

    Software engineering is constantly evolving, with new technologies and methodologies shaping the way we develop and maintain software applications. In recent years, artificial intelligence (AI), federated learning, and large language models have emerged as key technologies that are revolutionizing the field of software engineering.

    AI is being used to automate various aspects of the software development process, from code generation to bug detection and even project management. Machine learning algorithms are being trained on vast amounts of code repositories to learn patterns and predict potential issues before they arise. This not only saves time and resources for developers but also improves the overall quality and efficiency of software development.

    Federated learning, a decentralized approach to machine learning, is also making waves in the software engineering community. By training models locally on individual devices and then aggregating the results, federated learning allows for more efficient and privacy-preserving data processing. This is particularly useful for software development teams working on sensitive or proprietary projects, as it allows them to collaborate and share insights without compromising data security.

    Large language models, such as OpenAI’s GPT-3, are another game-changer for software engineering. These models are trained on massive amounts of text data and can generate human-like responses to queries and prompts. Developers can leverage these models to automate tasks like code completion, documentation generation, and even project planning. This not only speeds up the development process but also improves the overall readability and maintainability of code.

    By harnessing the power of AI, federated learning, and large language models, software engineering is entering a new era of innovation and efficiency. These technologies are enabling developers to build better software faster and more securely than ever before. As we continue to explore the possibilities of these cutting-edge technologies, the future of software engineering looks brighter than ever.
    #Advancing #Software #Engineering #Federated #Learning #Large #Language #Models

  • Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications

    Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications


    Price: $4.99
    (as of Dec 24,2024 12:04:18 UTC – Details)




    ASIN ‏ : ‎ B0DN34KH7Y
    Publisher ‏ : ‎ Wiley-IEEE Press; 1st edition (November 13, 2024)
    Publication date ‏ : ‎ November 13, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 31042 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 398 pages
    Page numbers source ISBN ‏ : ‎ 1394219210


    In the rapidly evolving field of artificial intelligence, model optimization is crucial for improving the efficiency and performance of AI models, especially in edge computing scenarios where resources are limited. One promising approach to model optimization is federated learning, which allows AI models to be trained across multiple decentralized devices without the need to transfer raw data to a central server.

    In this post, we will explore the various methods and techniques for optimizing AI models using federated learning architectures, frameworks, and applications. We will discuss the benefits and challenges of federated learning, as well as the different frameworks and tools available for implementing federated learning in edge AI scenarios.

    We will also highlight some real-world applications of federated learning, such as personalized healthcare, autonomous vehicles, and smart cities, where federated learning can be used to improve model performance while respecting data privacy and security.

    Overall, this post will provide a comprehensive overview of model optimization methods for efficient and edge AI using federated learning architectures, frameworks, and applications, and will showcase the potential of federated learning to revolutionize the way AI models are trained and deployed in edge computing environments.
    #Model #Optimization #Methods #Efficient #Edge #Federated #Learning #Architectures #Frameworks #Applications

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