Logistic Regression with BFV scheme
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Type: Question :grey_question:
- Dominant language
- C++
- Stars
- 1k
- Forks
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Description
I am trying to build a Logistic Regression model using BFV scheme to encrypt data. However, I cannot complete it because of floating point number in approximate sigmoid function and learning rate. Can someone help me solve this issue?
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
No file, test, or entry point is identified. Start by locating TenSEAL's BFV operations and the logistic-regression example or implementation, then establish how floating-point values in the sigmoid function and learning rate are handled; done means a working path or a clearly documented limitation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- cryptography, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100