FL + XGBoost

[jafermarq]

FL + XGBoost

• Title: Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
• Venue: EuroMLSys 2023
• Link to paper: https://arxiv.org/abs/2304.07537

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[Aml-Hassan-Abd-El-hamid]

Hi @jafermarq I'll be happy to work on this baseline. I'll work on re-producing the results from Table 2 p(6). I'll start by FedXGBllr and once the results for this one is ok, I'll be moving to simFL if I still got time. Regarding the datasets, I think I'll be adding most of them except for ( HIGGS, SUSY, real-sim, Year prediction MSD), The reason why I chose to exclude those is because of their bigger sizes of course but if I got the time I'll try with one of them.

[jafermarq]

Hi @Aml-Hassan-Abd-El-hamid, would you also include the centralized baseline?

[Aml-Hassan-Abd-El-hamid]

Hey @jafermarq Yes, I'll include the centralized baseline

[jafermarq]

great! @Aml-Hassan-Abd-El-hamid thanks for confirming. Then you are all set. I have ✅ all points in Step 1 & 2, added you as the assignee to this issue and moved this baseline to In Progress status. Following the link in the What happens next? section above you'll find a detailed step-by-step guide on how to start with the code. If you encounter some issues, please do not hesitate to reach out to me over the Flower Slack or reach out to other contributors of the Flower Summer of Reproducibility in the #summer-of-reproducibility channel.

Looking forward to seeing your baseline in action!

[jafermarq]

Congratulations @Aml-Hassan-Abd-El-hamid your baseline has been merged. We'll follow up with you soon to complete your Summer of Reproducibility journey 😄 !