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Maybe give motivations about why the paper should be implemented as a baseline.
Myself and my coauthors used Fed-HT (and Fed-IterHT) in recent work to generate sparse, federated models. These two aggregation strategies were effective in generating highly predictive models while also allowing for constrained sparsity (with some sparsity threshold), instead of just sparsity through regularization. We'd like to implement this baseline for easier implementation in the future through Flower. The goal with this baseline is to implement Fed-HT and Fed-IterHT as a custom Flower aggregation strategy and recreate the methodology of the paper, to include applications using simulated and benchmark datasets.
Is there something else you want to add?
No
Implementation
To implement this baseline, it is recommended to do the following items in that order:
HI @chancejohnstone 👋 It's great to see you want to add Fed-HT to the Flower Baselines collection ! Out of curiosity, what experiments from the paper are you planing to reproduce ? maybe parts of Fig 5? Please ping us if anytime if you have some doubts or if you encounter some problems.
@jafermarq That's a great idea; recreating parts of Figure 5 would be an effective way to showcase Fed-HT and Fed-IterHT. I would definitely try to start there.
@jafermarq Just wanted to give an update on progress for this baseline. Everything seems to be in order as far as the Fed-HT and FedIter-HT implementation is concerned. I have working examples using the datasets from the paper; the results don't currently match exactly due to a lack of knowledge on exact hyperparameters, e.g., weight decay, num_local_epochs, used for the plots. I have reached out to the authors to get those. Formatting and test scripts seem to run without error locally; I get a code rating at the end of running test-baseline.sh, but I'm not sure if that's the end of the script. The code fails the test check after commit and I'm not sure what the issue is. Happy to provide more details as necessary.
Paper
Tong, Q., Liang, G., Zhu, T. and Bi, J., 2020. Federated nonconvex sparse learning. arXiv preprint arXiv:2101.00052.
Link
https://arxiv.org/abs/2101.00052
Maybe give motivations about why the paper should be implemented as a baseline.
Myself and my coauthors used Fed-HT (and Fed-IterHT) in recent work to generate sparse, federated models. These two aggregation strategies were effective in generating highly predictive models while also allowing for constrained sparsity (with some sparsity threshold), instead of just sparsity through regularization. We'd like to implement this baseline for easier implementation in the future through Flower. The goal with this baseline is to implement Fed-HT and Fed-IterHT as a custom Flower aggregation strategy and recreate the methodology of the paper, to include applications using simulated and benchmark datasets.
Is there something else you want to add?
No
Implementation
To implement this baseline, it is recommended to do the following items in that order:
For first time contributors
first contribution
docPrepare - understand the scope
Verify your implementation
EXTENDED_README.md
that was created in your baseline directoryREADME.md
is ready to be run by someone that is no familiar with your code. Are all step-by-step instructions clear?README.md
and verify everything runs.The text was updated successfully, but these errors were encountered: