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FL_PyTorch is publicly available on GitHub

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Hi. I am one of the co-authors of FL PyTorch - an Optimization Research Simulator for Federated Learning. This Optimization Research Simulator for Federated Learning is publicly available on GitHub.

FL_PyTorch is a suite of open-source software written in python that builds on top of one of the most popular research Deep Learning (DL) frameworks PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast development, prototyping, and experimenting with new and existing FL optimization algorithms. Our system supports abstractions that provide researchers with sufficient flexibility to experiment with existing and novel approaches to advance the state-of-the-art. The work is in proceedings of the 2nd International Workshop on Distributed Machine Learning DistributedML 2021. The paper, presentation, and appendix are available in DistributedML’21 Proceedings (https://dl.acm.org/doi/abs/10.1145/3488659.3493775).

The project is distributed in open source form under Apache License Version 2.0. Code Repository: https://github.com/burlachenkok/flpytorch.

To become familiar with that tool, I recommend the following sequence of steps:

  1. Join our Slack Workspace https://fl-pytorch.slack.com/. The invitation Link.

  2. Look 8-minute presentation from DistributedML 2021 workshop available here: https://dl.acm.org/doi/abs/10.1145/3488659.3493775.

  3. Read the arXiv version of the paper (https://arxiv.org/abs/2202.03099). It contains a slightly more actual text in comparison to the original publication.

  4. Read https://github.com/burlachenkok/flpytorch/blob/main/README.md and perform all needed preliminary steps.

  5. Launch GUI fl_pytorch/gui/start.py and look around for a first impression.

  6. Look into FL_PyTorch at Rising Stars in AI Symposium 2022. The 25 minutes video talk: https://webcast.kaust.edu.sa/mediasite/Showcase/kaust/Presentation/600c852bedf94c8298f92d8c1703f8521d

  7. Read https://github.com/burlachenkok/flpytorch/blob/main/TUTORIAL.md, and during reading, start to play with the tool on Logistic Regression or synthetized quadratics

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