A privacy-preserving framework for hyperparameter tuning in federated learning, leveraging encrypted model updates to ensure data security and confidentiality during collaborative optimization. SecureTune enables efficient tuning while maintaining privacy, enhancing the robustness and trustworthiness of federated learning systems.
active 2024-11-20 → 2024-11-20 (UTC)
Activity over time
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Line chart, 1 days from 2024-11-20 to 2024-11-20. Pushes: 1 total, peak 1 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| GopalSinghRajput | 1 | 1 | 0 | 0 |
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Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.