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Tag: Federated

  • Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks

    Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks


    Price: $46.99
    (as of Dec 24,2024 02:50:46 UTC – Details)


    Customers say

    Customers find the book provides an accessible explanation of complex federated learning concepts, providing guidance on real-world implementations and examples across industries like healthcare and finance. They find it useful and interesting for both machine learning practitioners and leaders in AI-driven companies. The practical examples and code snippets are appreciated.

    AI-generated from the text of customer reviews


    Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks

    Federated learning is a decentralized machine learning approach where multiple clients collaborate to train a global model without sharing their data. This enables privacy-preserving machine learning and allows for training models on sensitive data without compromising user privacy.

    In this post, we will explore how to design and implement a federated learning system using Python. We will also discuss how to develop applications using existing frameworks such as TensorFlow Federated and PySyft.

    To get started with federated learning, you will need to set up a server-client architecture where the server coordinates the training process and aggregates model updates from the clients. The clients, on the other hand, train the model locally on their own data and send the updated model parameters to the server.

    To implement this architecture in Python, you can use frameworks like TensorFlow Federated, which provides high-level APIs for federated learning. Alternatively, you can use PySyft, a privacy-preserving deep learning framework that enables secure multi-party computation.

    Once you have set up the federated learning system, you can develop applications that leverage this technology for various use cases such as healthcare, finance, and IoT. For example, you can train a predictive model on medical data from different hospitals without sharing patient information, or you can build a fraud detection system that learns from multiple banks’ transaction data without compromising customer privacy.

    Overall, federated learning is a powerful technique that enables collaborative machine learning while preserving data privacy. By designing and implementing a federated learning system in Python and developing applications using existing frameworks, you can unlock the potential of this technology for a wide range of applications.
    #Federated #Learning #Python #Design #implement #federated #learning #system #develop #applications #existing #frameworks

  • Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

    Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)


    Price: $1,123.79
    (as of Dec 24,2024 02:03:10 UTC – Details)




    Publisher ‏ : ‎ Morgan & Claypool (December 19, 2019)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 189 pages
    ISBN-10 ‏ : ‎ 1681736977
    ISBN-13 ‏ : ‎ 978-1681736976
    Item Weight ‏ : ‎ 13.4 ounces
    Dimensions ‏ : ‎ 7.5 x 0.5 x 9.25 inches


    Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

    Federated learning is a cutting-edge machine learning technique that enables multiple parties to collaboratively train a shared model without sharing their private data. This innovative approach has been gaining traction in recent years due to its ability to address privacy concerns while still achieving high performance in machine learning tasks.

    In this post, we will explore the concept of federated learning and its applications in various industries. We will also delve into the challenges and opportunities associated with implementing federated learning in real-world scenarios.

    Join us as we dive into the world of federated learning and discover how this revolutionary technique is shaping the future of artificial intelligence and machine learning. Stay tuned for more insights and updates on this exciting topic!
    #Federated #Learning #Synthesis #Lectures #Artificial #Intelligence #Machine #Learning

  • Federated Learning with Python : Design and Implement a Federated Learning…

    Federated Learning with Python : Design and Implement a Federated Learning…



    Federated Learning with Python : Design and Implement a Federated Learning…

    Price : 25.00

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    Federated Learning with Python: Design and Implement a Federated Learning Model

    In this post, we will explore the concept of Federated Learning and how to design and implement a Federated Learning model using Python. Federated Learning is a decentralized approach to machine learning where multiple devices collaborate to train a global model without sharing their data.

    We will start by discussing the benefits of Federated Learning, such as privacy preservation and scalability. Then, we will walk through the steps to design a Federated Learning model, including setting up a server and client architecture, defining the model architecture, and implementing federated training algorithms.

    Next, we will dive into the code and showcase how to build a Federated Learning model using the PySyft library, which provides tools for secure and privacy-preserving machine learning. We will demonstrate how to set up a federated dataset, define a neural network model, and train the model using Federated Averaging.

    Finally, we will evaluate the performance of our Federated Learning model and discuss potential extensions and improvements. By the end of this post, you will have a solid understanding of Federated Learning and be able to design and implement your own Federated Learning models using Python. Stay tuned for the next part of this series, where we will explore advanced topics in Federated Learning and showcase real-world applications.
    #Federated #Learning #Python #Design #Implement #Federated #Learning..

  • Communication Efficient Federated Learning for Wireless Networks

    Communication Efficient Federated Learning for Wireless Networks


    Price: $169.99 – $129.98
    (as of Dec 15,2024 22:29:56 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2024 edition (February 20, 2024)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 190 pages
    ISBN-10 ‏ : ‎ 3031512650
    ISBN-13 ‏ : ‎ 978-3031512650
    Item Weight ‏ : ‎ 15.8 ounces
    Dimensions ‏ : ‎ 6.14 x 0.5 x 9.21 inches


    Communication Efficient Federated Learning for Wireless Networks

    Federated learning has emerged as a promising approach to train machine learning models across multiple decentralized devices, such as smartphones and IoT devices. However, in wireless networks where communication resources are limited, efficient communication is crucial for the success of federated learning.

    In this post, we will discuss how communication can be optimized for federated learning in wireless networks to improve efficiency and reduce latency.

    1. Model Compression: One way to reduce communication overhead in federated learning is to compress the model before sending it to the central server for aggregation. Techniques like quantization, pruning, and knowledge distillation can be used to compress the model without compromising its performance.

    2. Differential Privacy: Privacy-preserving techniques like differential privacy can be used to add noise to the gradients before sending them to the central server. This helps protect the privacy of the individual devices’ data while still allowing for effective model training.

    3. Adaptive Communication: Adaptive communication strategies, such as prioritizing communication with devices that have more relevant data or using different communication protocols based on the network conditions, can help reduce latency and improve efficiency in federated learning.

    4. Edge Computing: By performing model training and aggregation at the edge devices instead of sending all the data to a central server, communication overhead can be reduced. Edge computing can also help in reducing latency and improving the overall performance of federated learning in wireless networks.

    Overall, communication efficient federated learning is essential for enabling machine learning on resource-constrained devices in wireless networks. By implementing techniques like model compression, differential privacy, adaptive communication, and edge computing, we can optimize communication in federated learning and make it more efficient and scalable.
    #Communication #Efficient #Federated #Learning #Wireless #Networks

  • Achieving Federated and Self-Manageable Cloud Infrastructures: Theory and Practice

    Achieving Federated and Self-Manageable Cloud Infrastructures: Theory and Practice


    Price: $212.10
    (as of Dec 13,2024 19:05:08 UTC – Details)




    Publisher ‏ : ‎ Business Science Reference; 1st edition (May 31, 2012)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 490 pages
    ISBN-10 ‏ : ‎ 1466616318
    ISBN-13 ‏ : ‎ 978-1466616318
    Item Weight ‏ : ‎ 3.1 pounds
    Dimensions ‏ : ‎ 8.6 x 1.3 x 11.2 inches


    In today’s digital age, cloud infrastructures have become an essential component of modern businesses. However, as cloud environments continue to grow in complexity and scale, the need for federated and self-manageable cloud infrastructures has become increasingly important.

    Achieving federated and self-manageable cloud infrastructures requires a combination of theoretical understanding and practical implementation. In this post, we will explore the concepts behind federated and self-manageable cloud infrastructures, as well as the steps businesses can take to achieve these objectives.

    Federated cloud infrastructures involve the integration of multiple cloud environments to create a seamless and interconnected system. This allows businesses to access resources from different cloud providers, leading to improved scalability, flexibility, and cost-effectiveness. By federating their cloud infrastructures, businesses can avoid vendor lock-in and take advantage of the best services from various providers.

    Self-manageable cloud infrastructures, on the other hand, empower businesses to automate and optimize their cloud environments without human intervention. This includes tasks such as monitoring, provisioning, scaling, and optimizing resources based on workload demands. By implementing self-manageable cloud infrastructures, businesses can improve efficiency, reduce costs, and enhance overall performance.

    To achieve federated and self-manageable cloud infrastructures, businesses should consider the following steps:

    1. Assess their current cloud environment and identify areas for improvement.
    2. Implement cloud management tools and platforms that support federation and self-management capabilities.
    3. Develop policies and procedures for managing and governing federated cloud environments.
    4. Automate routine tasks and processes to enable self-management of cloud resources.
    5. Monitor and optimize cloud resources to ensure efficiency and cost-effectiveness.

    By following these steps and leveraging the latest technologies and best practices, businesses can successfully achieve federated and self-manageable cloud infrastructures. This will enable them to harness the full potential of cloud computing and stay competitive in today’s fast-paced digital landscape.
    #Achieving #Federated #SelfManageable #Cloud #Infrastructures #Theory #Practice

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