RRepoGEO

REPOGEO REPORT · LITE

chaoyanghe/Awesome-Federated-Learning

Default branch master · commit 779fd493 · scanned 5/12/2026, 9:17:44 AM

GitHub: 2,015 stars · 334 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 chaoyanghe/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
  • highabout#1
    Update repository description to clarify it's an awesome list

    Why:

    CURRENT
    FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
    COPY-PASTE FIX
    A curated list of federated learning publications and resources. This is an awesome list, not a library.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    (Create a LICENSE file in the repository root, e.g., MIT or Apache-2.0, to clearly state the terms of use for the content.)
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Set the 'Homepage' field in the repository settings to https://github.com/FedML-AI/FedML/blob/master/research/Awesome-Federated-Learning.md, as indicated in the README for the latest updates.

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 chaoyanghe/Awesome-Federated-Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Federated (TFF)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Federated (TFF) · recommended 2×
  2. Flower · recommended 2×
  3. PySyft · recommended 1×
  4. OpenMined's Syft · recommended 1×
  5. PyGrid · recommended 1×
  • CATEGORY QUERY
    Looking for a machine learning framework that supports decentralized training and data privacy.
    you: not recommended
    AI recommended (in order):
    1. PySyft
    2. OpenMined's Syft
    3. PyGrid
    4. PyDP
    5. TensorFlow Federated (TFF)
    6. Flower
    7. Substra

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

    Show full AI answer
  • CATEGORY QUERY
    What are the best libraries for production-ready federated learning with efficiency optimizations?
    you: not recommended
    AI recommended (in order):
    1. Flower
    2. FedML
    3. TensorFlow Federated (TFF)
    4. PySyft (OpenMined)
    5. LEAF (Learning in Federated Settings)
    6. FATE (Federated AI Technology Enabler)

    AI recommended 6 alternatives but never named chaoyanghe/Awesome-Federated-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 chaoyanghe/Awesome-Federated-Learning?
    pass
    AI did not name chaoyanghe/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 chaoyanghe/Awesome-Federated-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chaoyanghe/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 chaoyanghe/Awesome-Federated-Learning solve, and who is the primary audience?
    pass
    AI did not name chaoyanghe/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?

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chaoyanghe/Awesome-Federated-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