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pathakavani/DifferentiallyPrivateDeepLearning

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This project focuses on applying Differential Privacy to deep learning. ResNet 20 is used. For parallelization, Differentially Private Distributed Data Parallel (DPDDP) is used. We have also implemented the Differentially Private Importance Sampling algorithm from the DPIS paper by Wei, et al.

active 2024-10-022025-02-12 (UTC)

Complete coverage26,462 / 26,462 hourly files (100%) · 2 absent upstream2023-08-152026-08-21 (UTC)
Events
13
Pushes
9
Pull requests
0
Issues
0
Stars
0
Forks
1

Activity over time

Daily event counts in the loaded window

Line chart, 134 days from 2024-10-02 to 2025-02-12. Pushes: 9 total, peak 2 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.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
AdiJoshi296600
davidjoyme2200
pathakavani1100

Recent activity

Latest issues, pull requests and releases

No issue or PR events — this repo's activity is pushes only.

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.