This repository contains the code for OrbitalBrain, a distributed framework for training machine learning models in space. OrbitalBrain is designed to enable efficient in-space training of ML models by leveraging limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training.
OrbitalBrain is currently under active development. We are refining the codebase, improving documentation, and preparing the initial public release.
We plan to release the code in April 2026. Please check back for updates as we finalize the repository contents.
If you use our simulator in your work, we would appreciate a reference to the following paper:
Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra, "OrbitalBrain: A Distributed Framework For Training ML Models in Space," in Proceedings of the 1st New Ideas in Networked Systems (NINeS), February 2026.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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