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Tag: Federated

  • Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)

    Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)


    Price: $179.99 – $170.99
    (as of Dec 24,2024 11:20:58 UTC – Details)




    Publisher ‏ : ‎ Springer (April 10, 2025)
    Language ‏ : ‎ English
    ISBN-10 ‏ : ‎ 3031788400
    ISBN-13 ‏ : ‎ 978-3031788406
    Item Weight ‏ : ‎ 1.74 pounds


    Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)

    In today’s digital age, the collection and analysis of data have become essential for the development of artificial intelligence (AI) systems. However, concerns about privacy and data security have led to the development of new approaches to training AI models without compromising user privacy. Federated learning systems have emerged as a promising solution to this challenge.

    The book “Federated Learning Systems: Towards Privacy-Preserving Distributed AI” explores the concept of federated learning and its potential applications in various domains. Written by experts in the field, this book provides a comprehensive overview of the latest research and developments in federated learning systems.

    Federated learning allows AI models to be trained locally on individual devices or servers, with only the model updates being shared with a central server. This decentralized approach ensures that sensitive data remains on the user’s device, preserving privacy while still enabling the training of robust AI models.

    This book delves into the technical aspects of federated learning, including optimization algorithms, communication protocols, and privacy-preserving techniques. It also discusses real-world applications of federated learning, such as healthcare, finance, and smart cities.

    Whether you are a researcher, developer, or practitioner in the field of AI and machine learning, “Federated Learning Systems: Towards Privacy-Preserving Distributed AI” is a valuable resource for understanding the potential of federated learning in building privacy-preserving AI systems. Stay ahead of the curve and explore the future of distributed AI with this insightful book.
    #Federated #Learning #Systems #PrivacyPreserving #Distributed #Studies #Computational #Intelligence

  • Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health (Lecture Notes in Computer Science)

    Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health (Lecture Notes in Computer Science)


    Price: $54.99
    (as of Dec 24,2024 10:36:08 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2022 edition (October 9, 2022)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 220 pages
    ISBN-10 ‏ : ‎ 3031185226
    ISBN-13 ‏ : ‎ 978-3031185229
    Item Weight ‏ : ‎ 11.2 ounces
    Dimensions ‏ : ‎ 6.1 x 0.5 x 9.25 inches


    In today’s digital age, the intersection of artificial intelligence (AI) and healthcare has the potential to revolutionize the way we approach global health. With the rise of distributed, collaborative, and federated learning models, the possibilities for improving healthcare outcomes are endless.

    In a recent study published in the Lecture Notes in Computer Science, researchers explored the concept of leveraging these advanced learning models to create affordable AI solutions for resource diverse global health settings. By harnessing the power of distributed computing and collaboration, healthcare providers can access valuable insights and resources that were previously out of reach.

    One of the key advantages of distributed, collaborative, and federated learning is its ability to democratize access to AI technologies. By pooling together resources and knowledge from various sources, researchers and healthcare providers can work together to develop innovative solutions that address the unique challenges faced by different regions and populations.

    Furthermore, these models enable healthcare professionals to train AI algorithms using data from diverse populations, which can lead to more accurate and tailored healthcare interventions. By incorporating a federated learning approach, healthcare providers can ensure that the AI models are continuously improving and adapting to the ever-changing healthcare landscape.

    Ultimately, the integration of distributed, collaborative, and federated learning into healthcare systems has the potential to revolutionize the way we approach global health. By creating affordable AI solutions that are accessible to all, we can work towards a future where healthcare is more personalized, efficient, and effective for all individuals, regardless of their resources or location.
    #Distributed #Collaborative #Federated #Learning #Affordable #Healthcare #Resource #Diverse #Global #Health #Lecture #Notes #Computer #Science

  • Federated Learning Engineer Stainless Steel Insulated Tumbler

    Federated Learning Engineer Stainless Steel Insulated Tumbler


    Price: $20.99
    (as of Dec 24,2024 09:49:06 UTC – Details)



    Perfect for engineers working on federated learning, training models across devices, and protecting privacy. A funny and unique gift idea for engineering experts – “Don’t Panic! I’m A Professional Federated Learning Engineer”.
    Perfect for engineers working on federated learning, training models across devices, and protecting privacy.
    A funny and unique gift idea for engineering experts – “Don’t Panic! I’m A Professional Federated Learning Engineer”.
    Dual wall insulated: keeps beverages hot or cold
    Stainless Steel, BPA Free
    Leak proof lid with clear slider


    Are you a Federated Learning Engineer looking for the perfect tumbler to keep your drinks hot or cold during those long coding sessions? Look no further than our stainless steel insulated tumbler!

    This sleek and durable tumbler is perfect for taking with you on the go, whether you’re working from home or at the office. The double-wall insulation will keep your coffee piping hot or your water ice-cold for hours on end.

    Plus, the stylish design and convenient lid make this tumbler a must-have for any Federated Learning Engineer. Treat yourself to one today and elevate your drinkware game!
    #Federated #Learning #Engineer #Stainless #Steel #Insulated #Tumbler

  • Federated Learning and AI for Healthcare 5.0

    Federated Learning and AI for Healthcare 5.0


    Price: $360.00
    (as of Dec 24,2024 09:03:38 UTC – Details)




    ASIN ‏ : ‎ B0CG1C9L84
    Publisher ‏ : ‎ IGI Global (December 18, 2023)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 420 pages
    ISBN-13 ‏ : ‎ 979-8369310823
    Item Weight ‏ : ‎ 2.77 pounds
    Dimensions ‏ : ‎ 8.5 x 0.94 x 11 inches


    Federated Learning and AI for Healthcare 5.0: Advancing Personalized Medicine

    In the ever-evolving landscape of healthcare, the integration of federated learning and artificial intelligence (AI) has ushered in a new era of personalized medicine. Federated learning, a decentralized approach to machine learning, allows multiple entities to collaboratively train a shared model while keeping data localized and secure. This revolutionary technique has paved the way for advancements in healthcare 5.0, where patient-centric care is at the forefront.

    AI algorithms powered by federated learning have the ability to analyze vast amounts of patient data from various sources, including electronic health records, wearable devices, and genetic information. By pooling this data while maintaining patient privacy, healthcare providers can generate insights and predictions tailored to individual patients, leading to more accurate diagnoses and personalized treatment plans.

    One of the key benefits of federated learning in healthcare 5.0 is its ability to adapt and improve over time. As more data is collected and analyzed, AI algorithms can continuously learn and refine their predictions, ultimately leading to better outcomes for patients. Additionally, federated learning enables healthcare providers to leverage the collective knowledge of a global network of institutions, ensuring that the latest research and best practices are incorporated into patient care.

    As we enter the era of healthcare 5.0, the combination of federated learning and AI holds immense potential to transform the way we approach healthcare. By harnessing the power of decentralized machine learning, we can unlock new insights, drive innovation, and ultimately deliver more personalized and effective care to patients around the world.
    #Federated #Learning #Healthcare

  • Mastering ML System Design Interviews: The Definitive Guide to AI-Powered Machine Learning, AutoML, Edge Computing and Federated Learning

    Mastering ML System Design Interviews: The Definitive Guide to AI-Powered Machine Learning, AutoML, Edge Computing and Federated Learning


    Price: $49.99 – $29.90
    (as of Dec 24,2024 08:18:04 UTC – Details)


    From the Publisher

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    ASIN ‏ : ‎ B0DBYJ2VF3
    Publisher ‏ : ‎ Independently published (July 30, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 129 pages
    ISBN-13 ‏ : ‎ 979-8334579668
    Item Weight ‏ : ‎ 14.1 ounces
    Dimensions ‏ : ‎ 8.5 x 0.3 x 11 inches


    Mastering ML System Design Interviews: The Definitive Guide to AI-Powered Machine Learning, AutoML, Edge Computing and Federated Learning

    Are you preparing for a Machine Learning system design interview and feeling overwhelmed by the vast array of topics you need to cover? Look no further! In this comprehensive guide, we will delve into the key concepts and strategies that will help you ace your ML system design interviews.

    AI-Powered Machine Learning: Learn how to leverage the power of artificial intelligence to enhance your machine learning models and algorithms. Understand the latest advancements in AI technologies and how they can be applied to improve the performance of your ML systems.

    AutoML: Explore the world of automated machine learning (AutoML) and discover how it can streamline the process of building and deploying ML models. Learn about the different AutoML tools and platforms available, and how they can help you achieve faster and more efficient model development.

    Edge Computing: Dive into the world of edge computing and understand how it can revolutionize the way we deploy and run ML models. Explore the benefits of edge computing in terms of speed, efficiency, and scalability, and learn how to design ML systems that are optimized for edge devices.

    Federated Learning: Discover the power of federated learning and how it can enable secure and privacy-preserving collaborative model training across multiple devices and data sources. Learn about the challenges and opportunities of federated learning, and how to design ML systems that are federated learning-friendly.

    Whether you are a seasoned ML practitioner or just starting out in the field, this guide will provide you with the knowledge and skills you need to excel in ML system design interviews. Master the latest trends and technologies in AI-powered machine learning, AutoML, edge computing, and federated learning, and take your ML career to the next level. Good luck!
    #Mastering #System #Design #Interviews #Definitive #Guide #AIPowered #Machine #Learning #AutoML #Edge #Computing #Federated #Learning

  • Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security

    Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security


    Price: $375.00
    (as of Dec 24,2024 06:44:45 UTC – Details)




    ASIN ‏ : ‎ B0CPYN9XM2
    Publisher ‏ : ‎ IGI Global (February 14, 2024)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 370 pages
    ISBN-13 ‏ : ‎ 979-8369326398
    Item Weight ‏ : ‎ 2.57 pounds
    Dimensions ‏ : ‎ 8.5 x 0.88 x 11 inches


    In the ever-evolving world of healthcare, the integration of cutting-edge technologies is revolutionizing the way we approach patient care. Smart Healthcare 5.0 is the next frontier in this digital transformation, combining the power of IoT, federated learning, and cloud security to pioneer a new era of precision medicine and personalized healthcare.

    IoT devices are playing a crucial role in monitoring patient health in real-time, providing healthcare professionals with valuable data insights that can be used to deliver more personalized and efficient care. These devices can track vital signs, medication adherence, and even detect early warning signs of potential health issues, allowing for proactive interventions and improved patient outcomes.

    Federated learning is another game-changing technology that is reshaping the healthcare landscape. By enabling machine learning models to be trained across multiple decentralized devices while keeping data secure and private, federated learning allows for the development of more accurate predictive models without compromising patient privacy. This collaborative approach to data analysis is revolutionizing the way medical research is conducted and accelerating the pace of innovation in healthcare.

    Cloud security is paramount in ensuring the confidentiality and integrity of patient data in Smart Healthcare 5.0. With the increasing amount of sensitive health information being stored and accessed in the cloud, robust security measures are essential to protect against cyber threats and data breaches. Implementing encryption, access control, and regular security audits are just a few ways that healthcare organizations can safeguard patient data and maintain compliance with data privacy regulations.

    As we continue to push the boundaries of what is possible in healthcare, the integration of IoT, federated learning, and cloud security is paving the way for a new era of smart healthcare that is patient-centric, data-driven, and secure. By harnessing the power of these technologies, we can unlock new insights, improve clinical outcomes, and ultimately transform the way we deliver healthcare to patients around the world.
    #Pioneering #Smart #Healthcare #IoT #Federated #Learning #Cloud #Security

  • Federated learning

    Federated learning


    Price: $40.00
    (as of Dec 24,2024 06:00:52 UTC – Details)




    ASIN ‏ : ‎ B0CCCJ6GPZ
    Publisher ‏ : ‎ Independently published (July 24, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 40 pages
    ISBN-13 ‏ : ‎ 979-8853430846
    Reading age ‏ : ‎ 15 – 18 years
    Item Weight ‏ : ‎ 5.4 ounces
    Dimensions ‏ : ‎ 8.5 x 0.1 x 11 inches


    Federated learning is a cutting-edge approach in machine learning that allows multiple devices to collaborate on a shared model without sharing their data. This decentralized approach to training machine learning models offers improved privacy protection and scalability, making it ideal for industries such as healthcare, finance, and telecommunications.

    In federated learning, individual devices train a local model using their own data and then send only the model updates to a central server. The server aggregates these updates to improve the global model without ever seeing the raw data. This not only protects the privacy of user data but also reduces the need for large centralized datasets, making it more efficient and cost-effective.

    This collaborative learning approach has the potential to revolutionize the way machine learning models are trained and deployed, enabling organizations to harness the power of distributed data while maintaining data privacy and security. As federated learning continues to gain traction in various industries, it is poised to become a game-changer in the field of machine learning.
    #Federated #learning

  • Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)

    Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)


    Price: $99.00 – $59.79
    (as of Dec 24,2024 05:15:01 UTC – Details)



    Advances and Open Problems in Federated Learning (Foundations and Trends® in Machine Learning)

    Federated learning has emerged as a promising approach for training machine learning models on distributed data sources while ensuring data privacy and security. In recent years, significant advances have been made in the field, but there are still many open problems that need to be addressed.

    One of the key advances in federated learning is the development of more efficient and scalable algorithms. Researchers have proposed novel optimization techniques, such as federated averaging and federated optimization, to reduce communication overhead and improve convergence speed. These advancements have enabled federated learning to be applied to a wide range of applications, including healthcare, finance, and Internet of Things (IoT) devices.

    Another important development in federated learning is the integration of differential privacy mechanisms to protect sensitive data during the training process. By adding noise to the gradients or model updates, differential privacy ensures that individual data points cannot be inferred from the trained model. This has paved the way for federated learning to be used in highly regulated industries where data privacy is a top priority.

    Despite these advancements, there are still several open problems in federated learning that need to be addressed. One of the main challenges is the heterogeneity of data and computing resources across different devices. Designing algorithms that can handle this heterogeneity while ensuring model performance and convergence is a major research direction in the field.

    Another open problem is the robustness of federated learning against adversarial attacks. Since the training process is distributed across multiple devices, attackers can potentially manipulate the training data or model updates to compromise the integrity of the trained model. Developing defense mechanisms against such attacks is a critical area of research in federated learning.

    In conclusion, federated learning has made significant strides in recent years, but there are still many challenges that need to be overcome. By addressing these open problems, researchers can continue to advance the field and unlock the full potential of federated learning in real-world applications.
    #Advances #Open #Problems #Federated #Learning #Foundations #Trendsr #Machine #Learning

  • Federated Learning: Privacy and Incentive (Lecture Notes in Computer Science)

    Federated Learning: Privacy and Incentive (Lecture Notes in Computer Science)


    Price: $84.99
    (as of Dec 24,2024 04:27:17 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2020 edition (November 26, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 296 pages
    ISBN-10 ‏ : ‎ 3030630757
    ISBN-13 ‏ : ‎ 978-3030630751
    Item Weight ‏ : ‎ 1.01 pounds
    Dimensions ‏ : ‎ 6.1 x 0.67 x 9.25 inches


    Federated Learning: Privacy and Incentive (Lecture Notes in Computer Science)

    Federated Learning is an emerging field in machine learning that aims to train models across multiple decentralized devices which keep data private on the device itself, rather than sending it to a central server. This approach has the potential to address privacy concerns associated with traditional centralized machine learning models.

    In this lecture notes series in Computer Science, we delve into the key concepts of Federated Learning, focusing on the crucial aspects of privacy and incentive. We explore how Federated Learning enables data owners to collaborate on model training without sharing their raw data, thus preserving privacy.

    Furthermore, we discuss the importance of providing incentives for participants to actively participate in Federated Learning. Incentive mechanisms play a vital role in motivating users to contribute their data and computational resources to the model training process. We examine various incentive schemes and their implications for the success of Federated Learning systems.

    Join us as we explore the intersection of privacy and incentive in Federated Learning, and learn how this innovative approach is shaping the future of machine learning.
    #Federated #Learning #Privacy #Incentive #Lecture #Notes #Computer #Science

  • Federated Learning: A Comprehensive Overview of Methods and Applications

    Federated Learning: A Comprehensive Overview of Methods and Applications


    Price: $30.39
    (as of Dec 24,2024 03:39:32 UTC – Details)




    ASIN ‏ : ‎ B0B62RZ5LS
    Publisher ‏ : ‎ Springer (July 7, 2022)
    Publication date ‏ : ‎ July 7, 2022
    Language ‏ : ‎ English
    File size ‏ : ‎ 53295 KB
    Text-to-Speech ‏ : ‎ Enabled
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 919 pages


    Federated Learning: A Comprehensive Overview of Methods and Applications

    Federated learning is a cutting-edge machine learning technique that allows multiple parties to collaboratively train a shared model without sharing their raw data. This decentralized approach to training models has gained popularity in recent years due to its privacy-preserving capabilities and scalability. In this post, we will provide a comprehensive overview of federated learning, including its methods and applications.

    Methods of Federated Learning:

    1. Horizontal Federated Learning: In this approach, multiple clients have access to the same features but have different samples of data. The clients train a shared model by averaging their local updates.

    2. Vertical Federated Learning: In this approach, clients have different features but share a common set of samples. The clients train separate models on their respective features and combine them to create a global model.

    3. Federated Transfer Learning: This method involves transferring knowledge from a pre-trained model to a federated learning setting, where clients fine-tune the model on their local data.

    Applications of Federated Learning:

    1. Healthcare: Federated learning is widely used in healthcare for training models on sensitive patient data without compromising privacy. Hospitals can collaborate to train models for disease diagnosis, drug discovery, and personalized treatment recommendations.

    2. Finance: Banks and financial institutions can use federated learning to build fraud detection models while maintaining the privacy of customer transaction data. By aggregating local updates from multiple banks, a robust fraud detection model can be trained.

    3. Internet of Things (IoT): Federated learning is ideal for training models on data generated by IoT devices, such as smart sensors and wearable devices. By training models locally on the devices and aggregating the updates, personalized recommendations and predictive maintenance strategies can be implemented.

    4. Edge Computing: Federated learning is well-suited for edge computing environments where data is generated and processed locally on devices. By training models on the edge devices and only sending model updates to a central server, real-time predictions can be made without compromising data privacy.

    In conclusion, federated learning offers a promising solution for collaborative model training while preserving data privacy and security. By understanding the various methods and applications of federated learning, organizations can leverage this technique to build robust and scalable machine learning models.
    #Federated #Learning #Comprehensive #Overview #Methods #Applications

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