CmpE-491-Baris-Basmak

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CmpE 491 Federated ML for Covid-19

Updates

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Before 26.10.2020

Read the articles

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• Federated Learning Challenges, methods, and future directions https://arxiv.org/pdf/1908.07873.pdf, Learned about:

  • Federated Learning Concepts
  • Network Topologies used in FL
  • Compression Schemes
  • Difficulty that comes with non i.i.d. data
  • Privacy for Federated Learning

• Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data https://www.nature.com/articles/s41598-020-69250-1, Learned about

  • Cyclic institutional incremental learning
  • Institutional incremental learning
  • Catastrophic forgetting and how FL becomes a solution for catastrophic forgetting
  • Gained insight about experimenting methodologies used

• Federated Learning: Collaborative Machine Learning without Centralized Training Data https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

• Differential Privacy for Everyone https://download.microsoft.com/download/D/1/F/D1F0DFF5-8BA9-4BDF-8924-7816932F6825/Differential_Privacy_for_Everyone.pdf

26.10.2020-01.11.2020

Read the article

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• Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf
  • 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

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• Federated Learning for Image Classification https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification
• Custom Federated Algorithms, Part 2: Implementing Federated Averaging https://www.tensorflow.org/federated/tutorials/custom_federated_algorithms_2

Plans for the Upcoming Week

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• 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

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• Learned aobut TensorFlow and TensorFlow Federated
• Worked on basic CIFAR10 model: https://colab.research.google.com/drive/1NFW_YXpptkM6O0lZ-xQN-PE7DOjo1k2M#scrollTo=DFSsHIeEV9k8

Plans for the upcoming week

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

  • https://arxiv.org/pdf/2009.10401.pdf

• 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

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• 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.
  • https://arxiv.org/pdf/2009.10401.pdf

Plans for the upcoming week

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• 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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• Read the paper :Averaging_Is_Probably_Not_the_Optimum_Way_of_Aggregating_Parameters_in_Federated_Learning https://www.researchgate.net/publication/339880491_Averaging_Is_Probably_Not_the_Optimum_Way_of_Aggregating_Parameters_in_Federated_Learning/fulltext/5e6a2cde92851c20f322812c/Averaging-Is-Probably-Not-the-Optimum-Way-of-Aggregating-Parameters-in-Federated-Learning.pdf
• Read the paper : Client-Edge-Cloud Hierarchical Federated Learning https://arxiv.org/pdf/1905.06641.pdf Read the paper:Dynamic FL https://www.researchgate.net/publication/339399233 Read a paper about Coded Federated Learning
• Tried to get more insight about using TensorFlow Federated