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Related Experiment Videos

Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted

Yang Chen, Xiaoyan Sun, Yaochu Jin

    IEEE Transactions on Neural Networks and Learning Systems
    |January 4, 2020
    PubMed
    Summary

    This study enhances federated learning by using asynchronous client updates and temporally weighted aggregation. The new method reduces communication costs and improves central model accuracy, benefiting data privacy.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Science

    Background:

    • Federated learning trains a central model without client data transfer, preserving privacy.
    • Reducing client-server communication is crucial due to limited bandwidth on end devices.

    Purpose of the Study:

    • To enhance federated learning efficiency and accuracy.
    • To address communication bottlenecks in federated deep learning.

    Main Methods:

    • Proposed an asynchronous learning strategy for clients, updating deep neural network (DNN) layers at different frequencies.
    • Introduced a temporally weighted aggregation strategy on the server, utilizing historical local models.

    Main Results:

    • The enhanced federated learning technique demonstrated superior performance compared to baseline algorithms.

    Related Experiment Videos

  • Achieved significant improvements in both communication cost reduction and central model accuracy.

    • Conclusions:

    • The proposed asynchronous federated deep learning method effectively optimizes communication and enhances model performance.
    • This approach offers a promising solution for efficient and accurate federated learning systems.