RRepoGEO

REPOGEO REPORT · LITE

innovation-cat/Awesome-Federated-Machine-Learning

Default branch master · commit 831ef49f · scanned 5/23/2026, 12:03:00 PM

GitHub: 2,078 stars · 288 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 innovation-cat/Awesome-Federated-Machine-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 the chosen open-source license text (e.g., MIT, Apache-2.0, GPL-3.0).
  • highhomepage#2
    Set the repository homepage URL to awesome.re

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://awesome.re
  • mediumabout#3
    Refine the 'About' description to explicitly mention 'awesome list'

    Why:

    CURRENT
    Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
    COPY-PASTE FIX
    A comprehensive awesome list and curated collection of resources for federated learning, including research papers, books, codes, tutorials, and videos.

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 innovation-cat/Awesome-Federated-Machine-Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/federated
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/federated · recommended 2×
  2. OpenMined/PySyft · recommended 2×
  3. Google AI Blog · recommended 1×
  4. Google Scholar · recommended 1×
  5. OpenMined · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources to learn about federated machine learning concepts?
    you: not recommended
    AI recommended (in order):
    1. Google AI Blog
    2. Google Scholar
    3. TensorFlow Federated (TFF) (tensorflow/federated)
    4. OpenMined
    5. PySyft (OpenMined/PySyft)
    6. PyGrid (OpenMined/PyGrid)
    7. IBM Federated Learning (IBM/federated-learning-lib)
    8. NVIDIA FLARE (NVIDIA/NVFlare)
    9. Coursera
    10. edX

    AI recommended 10 alternatives but never named innovation-cat/Awesome-Federated-Machine-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable training machine learning models collaboratively while preserving data privacy?
    you: not recommended
    AI recommended (in order):
    1. PySyft (OpenMined/PySyft)
    2. OpenMined's SyMPC (OpenMined/SyMPC)
    3. Flower (adap/flower)
    4. TensorFlow Federated (TFF) (tensorflow/federated)
    5. Intel homomorphic encryption (HE) toolkit
    6. FATE (Federated AI Technology Enabler) (FederatedAI/FATE)
    7. CrypTen (facebookresearch/CrypTen)

    AI recommended 7 alternatives but never named innovation-cat/Awesome-Federated-Machine-Learning. This is the gap to close.

    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 innovation-cat/Awesome-Federated-Machine-Learning?
    pass
    AI named innovation-cat/Awesome-Federated-Machine-Learning explicitly

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

  • If a team adopts innovation-cat/Awesome-Federated-Machine-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named innovation-cat/Awesome-Federated-Machine-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 innovation-cat/Awesome-Federated-Machine-Learning solve, and who is the primary audience?
    pass
    AI did not name innovation-cat/Awesome-Federated-Machine-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

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innovation-cat/Awesome-Federated-Machine-Learning — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite