Answers.Body: <p>There are some works that do this comparison. Briefly, it's been observed that the performance of models trained via FL drops as data distributions between participating agents differ. When data is IID-like though, performance is comparable to centralized training. Some works that I'm aware of are as follows:</p>
  <ol>
  <li><a href="https://arxiv.org/abs/1910.07796" rel="nofollow noreferrer">Overcoming Forgetting in Federated Learning on Non-IID Data</a></li>
  <li><a href="https://arxiv.org/abs/2010.15582" rel="nofollow noreferrer">Improving Accuracy of Federated Learning in Non-IID Settings</a></li>
  <li><a href="http://Federated%20Learning%20with%20Non-IID%20Data" rel="nofollow noreferrer">Federated Learning with Non-IID Data</a></li>
  </ol>
  <p>There are probably many more around. It's an active area of research.</p>
  
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Answers.Text: Thanks for your information! one more question, do you have specific resources for "performance is comparable to centralized training."?
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Answers.Text: that’s what the results indicate. look at the experiments of paper 2 and 3.
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Body: <p>How would the performance of federated learning (FL) compare to the performance of centralized machine learning (ML), when the data is independent and identically distributed (i.i.d.)?</p>
  <p>Moreover, what is the difference in the performance of FL when the data is i.i.d. as compared to non-i.i.d?</p>
  
Tags: <machine-learning><reference-request><performance><federated-learning><iid>
Title: How would the performance of federated learning compare to the performance of centralized machine learning when the data is i.i.d.?
UserDisplayName: Jared
ThreadText: # How would the performance of federated learning compare to the performance of centralized machine learning when the data is i.i.d.?
  
  (Asked by: username_0 on 2021-03-23)
  
  How would the performance of federated learning (FL) compare to the performance of centralized machine learning (ML), when the data is independent and identically distributed (i.i.d.)?
  
  Moreover, what is the difference in the performance of FL when the data is i.i.d. as compared to non-i.i.d?
  
  **Tags:** machine-learning, reference-request, performance, federated-learning, iid
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-03-23. Score: 1 | Accepted answer
  
  There are some works that do this comparison. Briefly, it's been observed that the performance of models trained via FL drops as data distributions between participating agents differ. When data is IID-like though, performance is comparable to centralized training. Some works that I'm aware of are as follows:
  
  1. [Overcoming Forgetting in Federated Learning on Non-IID Data](https://arxiv.org/abs/1910.07796)
  2. [Improving Accuracy of Federated Learning in Non-IID Settings](https://arxiv.org/abs/2010.15582)
  3. [Federated Learning with Non-IID Data](http://Federated%20Learning%20with%20Non-IID%20Data)
  
  There are probably many more around. It's an active area of research.
  
  > **Comments:**
  > **username_0**: Thanks for your information! one more question, do you have specific resources for "performance is comparable to centralized training."?  
  > **username_1**: that’s what the results indicate. look at the experiments of paper 2 and 3.  
  
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