Uniform Client Selction Over Time Series Medical Task #1121
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With @luciainnocenti, we completed this tutorial, where we evaluated Fed-Biomed using uniform client selection using a medical time series dataset over a large number of nodes.
The dataset used is the Replace-BG dataset. More information can be found here:
The preprocessing is inspired by the following paper:
The code is inspired by the following GitHub repository:
In this tutorial, we can add up to 202 nodes and start up to 180 nodes (uniform selection of 10%) for federated training. We then evaluate the global model over 22 clients. The training runs for 400 rounds, with the following requirements: RAM > 80 GB and 64 cores. This setup takes around 10 hours to complete.
![image](https://private-user-images.githubusercontent.com/41542771/335103494-491ea892-aa36-4b1e-a7ea-755df7f66d65.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.xc4C4VC-tfLFzqgaWLItctcX6N93tgnA6UqL4hkylVM)
Here the result:
However, in the notebook, it is possible to decrease the number of started nodes, we will use 10 nodes for training (uniform selection of 50%), and for testing for 5 rounds.