RRepoGEO

REPOGEO REPORT · LITE

poga/awesome-federated-learning

Default branch master · commit 3f541282 · scanned 6/11/2026, 10:33:12 AM

GitHub: 547 stars · 93 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface poga/awesome-federated-learning, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a suitable open-source license for content, such as CC-BY-4.0.
  • highreadme#2
    Enhance README with a clear scope and value proposition

    Why:

    COPY-PASTE FIX
    Add a dedicated 'About This List' or 'Scope' section after the initial description, explicitly stating what makes this list valuable (e.g., its focus on specific sub-areas, depth of coverage, or target audience) and what it aims to achieve. For example: 'This curated list aims to be the definitive collection of foundational and cutting-edge research papers, surveys, and key resources in federated learning, with a particular emphasis on privacy-preserving techniques and applications in medical data. Unlike general overviews, we focus on providing direct links to academic works and comprehensive surveys to aid researchers and practitioners.'
  • mediumtopics#3
    Add 'awesome-list' and 'curated-list' to repository topics

    Why:

    CURRENT
    deep-learning, federated-learning, medical-data, privacy
    COPY-PASTE FIX
    deep-learning, federated-learning, medical-data, privacy, awesome-list, curated-list, resources

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface poga/awesome-federated-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenMined/PySyft
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenMined/PySyft · recommended 1×
  2. OpenMined/PyGrid · recommended 1×
  3. tensorflow/federated · recommended 1×
  4. Intel/he-toolkit · recommended 1×
  5. IntelAI/nGraph-HE · recommended 1×
  • CATEGORY QUERY
    How can I implement privacy-preserving machine learning across distributed datasets?
    you: not recommended
    AI recommended (in order):
    1. OpenMined PySyft (OpenMined/PySyft)
    2. PyGrid (OpenMined/PyGrid)
    3. TensorFlow Federated (tensorflow/federated)
    4. Intel homomorphic encryption Toolkit (Intel/he-toolkit)
    5. nGraph-HE (IntelAI/nGraph-HE)
    6. Microsoft SEAL (microsoft/SEAL)
    7. IBM Federated Learning
    8. MP-SPDZ (data61/MP-SPDZ)
    9. FRESCO (FRESCO-Framework/FRESCO)
    10. Google's Differential Privacy Library (google/differential-privacy)
    11. OpenMined PyDP (OpenMined/PyDP)

    AI recommended 11 alternatives but never named poga/awesome-federated-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources and surveys on secure distributed deep learning?
    you: not recommended
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of poga/awesome-federated-learning?
    pass
    AI did not name poga/awesome-federated-learning — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts poga/awesome-federated-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named poga/awesome-federated-learning explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo poga/awesome-federated-learning solve, and who is the primary audience?
    pass
    AI did not name poga/awesome-federated-learning — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of poga/awesome-federated-learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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  • Brand-free category queries5 vs 2 in Lite
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