[WIP] Federated learning

[maximegmd]

Context

What is the purpose of this PR? Is it to

• add a new feature
• fix a bug
• update tests and/or documentation
• other (please add here)

Please link to any issues this PR addresses.

Changelog

What are the changes made in this PR?

• Adds a new recipe federation to synchronize federated learning jobs.
• Adds new components federation and federator, yes naming is confusing, open to ideas :)
• Adds new protocol based on HTTP to dispatch training and synchronize.
• Implement DiLiCo federated learning.
• Bootstraping, federator should send the model wieghts and configuration.
• Support for distributed recipes.
• Implement FedAvg.
• Run evals before and after each synchronization, report results to the centralized node.
• Implement failure recovery mechanisms.
• Create tests for merge implementations (DiLiCo, FedAvg).
• LoRA support.
• Adapt checkpointing for the federator.
• Implement weighted contribution based on processed tokens per participant.

Test plan

Please make sure to do each of the following if applicable to your PR. If you're unsure about any one of these just ask and we will happily help. We also have a contributing page for some guidance on contributing.

• run pre-commit hooks and linters (make sure you've first installed via pre-commit install)
• add unit tests for any new functionality
• update docstrings for any new or updated methods or classes
• run unit tests via pytest tests
• run recipe tests via pytest tests -m integration_test
• manually run any new or modified recipes with sufficient proof of correctness
• include relevant commands and any other artifacts in this summary (pastes of loss curves, eval results, etc.)

UX

If your function changed a public API, please add a dummy example of what the user experience will look like when calling it.
Here is a docstring example
and a tutorial example

• I did not change any public API
• I have added an example to docs or docstrings

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchtune/2418

• 📄 Preview Python docs built from this PR

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[krammnic]

Overall looks like a good start! Thanks!