UVeQFed: Universal Vector Quantization for Federated Learning

@article{Shlezinger2021UVeQFedUV,
  title={UVeQFed: Universal Vector Quantization for Federated Learning},
  author={Nir Shlezinger and M. Chen and Yonina C. Eldar and H. V. Poor and Shuguang Cui},
  journal={IEEE Transactions on Signal Processing},
  year={2021},
  volume={69},
  pages={500-514}
}
  • Nir Shlezinger, M. Chen, +2 authors Shuguang Cui
  • Published 2021
  • Computer Science, Mathematics
  • IEEE Transactions on Signal Processing
    • Traditional deep learning models are trained at a centralized server using data samples collected from users. Such data samples often include private information, which the users may not be willing to share. Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their data. FL consists of an iterative procedure, where in each iteration the users train a copy of the learning model locally. The server then collects the individual updates… CONTINUE READING
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    Methods Citations
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