NVIDIA/NVFlare - Open Source PR Review Scorecard

NVIDIA Federated Learning Application Runtime Environment

C-Rank Grade: S (Elite) - 74/100

External PR Merge Rate: 80%

Response Time: 9h

First Timer Success: 60%

Frequently Asked Questions

Is NVIDIA/NVFlare welcoming to first-time open-source contributors?

NVIDIA/NVFlare has a recorded first-timer success rate of 60.0%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in NVIDIA/NVFlare respond to incoming external pull requests in approximately 9.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 73.8 C-Rank™ score (S Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 73.8 places NVIDIA/NVFlare in the S tier.

What is the external contributor pull request merge rate for NVIDIA/NVFlare?

The external contributor pull request merge rate for NVIDIA/NVFlare is 80.2%, based on public PR activity from non-core contributors.

Are there Good First Issues available in NVIDIA/NVFlare?

NVIDIA/NVFlare currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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NVIDIA/NVFlareS•Elite981
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NVIDIA

NVIDIA/NVFlare

981
Active (30d)S•Elite(74/100)Python

NVIDIA Federated Learning Application Runtime Environment

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AI Maintainer Review Guidelines

Review Persona

Active Open-Source Maintainer

Warmth Score
7.8/10
Patience Score
8.2/10
Nitpick Rate
20%

Active maintainer team in NVIDIA/NVFlare welcoming community pull requests and bug fixes.

Top PR Submission Do's

  • •Ensure code complies with repository style conventions
  • •Keep PRs focused and well-documented

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit unlinked PRs without context
  • •Do not break existing automated test suites
Response Velocity
9 hours
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
80.2%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
60.0%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
20 core
Highly collaborative maintainer core
Diagnostic Health HUD
80.2%
Merge Gauge
60.0%
1st-Timer
Community Vibe73/100

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Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for NVIDIA/NVFlare
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/NVIDIA/NVFlare)](https://getmerged.abhishekco.de/NVIDIA/NVFlare?utm_source=github&utm_medium=badge)

Active Good First Issues (1)

View on GitHub

NVFlare provides FedAvg and several approaches for federated learning under heterogeneous/non-IID data, including FedProx and SCAFFOLD. However, FedAvg aggregation is primarily driven by client sample size, which may not adequately represent the information value of individual clients when their local data distributions differ significantly. A large client whose distribution is highly redundant with other participants can dominate aggregation, while a smaller client containing underrepresented patterns may have limited influence on the resulting global model. It would be useful to support an optional heterogeneity-aware dynamic weighting strategy for FedAvg. Instead of determining aggregation coefficients only from the number of local samples, the strategy could combine sub-linear sample-size weighting with a configurable heterogeneity or representation score. For example, a client-side distribution descriptor could be compared with an aggregated reference distribution using a measure

📅 Opened Aug 26, 2026💬 6 comments
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for NVIDIA/NVFlare

When evaluating whether to contribute to NVIDIA/NVFlare, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of NVIDIA/NVFlare acknowledge new external contributions in approximately 9 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 80.2% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to NVIDIA/NVFlare

01

Is NVIDIA/NVFlare welcoming to first-time open-source contributors?

NVIDIA/NVFlare has a recorded first-timer success rate of 60.0%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~9 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 73.8 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 73.8 places NVIDIA/NVFlare in the Elite tier.

04

What is the external contributor pull request merge rate for NVIDIA/NVFlare?

The external pull request merge rate is 80.2%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in NVIDIA/NVFlare?

Yes, NVIDIA/NVFlare currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for NVIDIA/NVFlare are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.