RRepoGEO

REPOGEO REPORT · LITE

securefederatedai/openfederatedlearning

Default branch develop · commit a4f8ae1e · scanned 6/3/2026, 4:41:17 AM

GitHub: 839 stars · 235 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 securefederatedai/openfederatedlearning, 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
  • highreadme#1
    Move the project's archived status and migration note to the very top of the README

    Why:

    CURRENT
    The 'Note: The Open Federated Learning project...' is currently placed after badges and a navigation bar.
    COPY-PASTE FIX
    Move the entire 'Note: The Open Federated Learning project (formerly known as OpenFL) is no longer under active development and will soon be archived. For existing users looking for ongoing support, we recommend the community transitions to Flower framework using the migration guide created in collaboration between our teams.' block to the absolute top of the README, before any other content or links.
  • highabout#2
    Update the GitHub repository description to reflect the archived status and migration recommendation

    Why:

    CURRENT
    An Open Framework for Federated Learning.
    COPY-PASTE FIX
    An archived Python framework for Federated Learning. Users are recommended to transition to the Flower framework.
  • mediumtopics#3
    Add topics reflecting the project's archived status and migration guidance

    Why:

    CURRENT
    federated-analytics, federated-evaluation, federated-learning, privacy-preserving-ml, python, trusted-execution-environment
    COPY-PASTE FIX
    federated-analytics, federated-evaluation, federated-learning, privacy-preserving-ml, python, trusted-execution-environment, archived, migration-guide, flower-framework

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 securefederatedai/openfederatedlearning
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. tensorflow/federated · recommended 1×
  3. adap/flower · recommended 1×
  4. openfheorg/openfhe · recommended 1×
  5. microsoft/SEAL · recommended 1×
  • CATEGORY QUERY
    How can I train machine learning models collaboratively while keeping sensitive data private?
    you: not recommended
    AI recommended (in order):
    1. PySyft (OpenMined/PySyft)
    2. TensorFlow Federated (tensorflow/federated)
    3. Flower (adap/flower)
    4. OpenFHE (openfheorg/openfhe)
    5. Microsoft SEAL (microsoft/SEAL)
    6. HElib (homenc/HElib)
    7. Google's Differential Privacy Library (google/differential-privacy)
    8. Opacus (pytorch/opacus)
    9. Graphene-SGX (oscarlab/graphene)
    10. MesaTEE (mesatee/mesatee)

    AI recommended 10 alternatives but never named securefederatedai/openfederatedlearning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for Python frameworks to implement privacy-preserving distributed machine learning experiments.
    you: not recommended
    AI recommended (in order):
    1. PySyft
    2. TensorFlow Privacy
    3. FATE
    4. OpenFL
    5. CrypTen
    6. Diffprivlib

    AI recommended 6 alternatives but never named securefederatedai/openfederatedlearning. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 securefederatedai/openfederatedlearning?
    pass
    AI did not name securefederatedai/openfederatedlearning — 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 securefederatedai/openfederatedlearning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named securefederatedai/openfederatedlearning 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 securefederatedai/openfederatedlearning solve, and who is the primary audience?
    pass
    AI did not name securefederatedai/openfederatedlearning — 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

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MARKDOWN (README)
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securefederatedai/openfederatedlearning — RepoGEO report