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CmpE 491 Federated ML for Covid-19


Before 26.10.2020

Read the articles


Read the article

  • Communication-Efficient Learning of Deep Networks from Decentralized Data
    • How cost functions of a Federated Learning differs from normal ML cost functions
    • Federated Averaging (FedAvg) Algorithm
    • System heterogenity and ways to tackle this problem (FedProx algorithm, weighted averaging)
Followed the Tutorials

Plans for the Upcoming Week

  • I'm planning on getting more Familiar with TensorFlow by watching tutorials and making small models.
  • After learning more about TensorFlow, I'm planning on familiarizing myself more with Tensor Flow Federated and making the model for MNIST (or maybe CIFAR) image classification from scratch.
  • I'm planning on reading more articles about Federated Learning.
03.11.2020 - 09.11.2020 Done

Plans for the upcoming week

  • Search for Covid-19 Databases that can be used.

  • Read at least 3 papers about Federated Learning

  • Practice more with tensor flow federated:

    • with more complex models
    • with different datasets
    09.11.2020 - 16.11.2020 Done

  • Implemented a Client Server architecture from scratch using keras, tf and python.
  • Briefly searched for databases that could be used for the project: Federated Learning for Covid-19.
Plans for the upcoming week

  • Implement a deeper model using ResNet or similar architecture.
  • Learn more about TFF since the client-server architecture I implemented has overhead when distributing the model weights and reduces speed.
  • Literature scan for layer specific training using Federated Learning.
16.11.2020 - 23.11.2020


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