Tisha-runwal / Personalized-Federated-Learning-for-Privacy--Preserving-and-Scalable-IoT-Driven-Smart-Healthcare
A Personalized Federated Learning (PFL-HCare) framework for IoT healthcare. Features MAML meta-learning, Differential Privacy (RDP), and gradient quantization for efficiency. Includes a React/FastAPI dashboard for real-time monitoring.
Why this job: works in the areas we cover, but does not say which job it does
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 6 months ago
- Stars
- 266
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 59% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- None
- No issues filed
- Pull requests
- 0 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 59%
- TypeScript 41%
- JavaScript <1%
- HTML <1%
Topics
- differential-privacy
- fastapi
- federated-learning
- iot-healthcare
- machine-learning
- maml
- meta-learning
- privacy-preserving
- pytorch
- react
- smart-healthcare
Project record
- JobOther AI tools
- StatusListed
- Commits4
- Forks3
- Watching1
- Stars in 28 days0
- CreatedApr 5, 2026
- First seen by usNov 7, 2026
- CheckedNov 8, 2026