add training for LSTM models completely on device
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- Dominant language
- Python
- Stars
- 5k
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Description
🚀 The feature, motivation and pitch
I've seen lots of examples of tuning embeddings, centroids and adding weights to the dense heads of an LSTM model but I want to know if it's possible to basically pass in data for the model to be trained on fully on device.
This way there are no privacy concerns and it's all personalized on device. Currently I've seen use cases for LLM's and things that aren't personalized so you can keep the underlying model frozen but that won't fit my usecase.
Alternatives
With powerful devices coming out, and if we can have the model be trained on off hours when the users device is charging and plugged in I'm wondering if this is feasible, given that the data might not be large.
Additional context
No response
RFC (Optional)
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no implementation files, tests, or entry points. Start by reviewing ExecuTorch's existing on-device training support and LSTM-related capabilities; done would require an agreed scope for private, personalized training and a concrete validation plan.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- embedded-iot, machine-learning, mobile-dev
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 18/100