RRepoGEO

REPOGEO REPORT · LITE

FederatedAI/FATE

Default branch master · commit 5a06d9e4 · scanned 5/14/2026, 12:17:29 AM

GitHub: 6,072 stars · 1,568 forks

AI VISIBILITY SCORE
60 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 FederatedAI/FATE, 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
  • hightopics#1
    Add topics for collaborative and distributed AI training

    Why:

    CURRENT
    algorithm, fate, federated-learning, machine-learning, privacy-preserving
    COPY-PASTE FIX
    algorithm, fate, federated-learning, machine-learning, privacy-preserving, collaborative-ai, distributed-training
  • mediumhomepage#2
    Add the official project homepage URL

    Why:

    COPY-PASTE FIX
    https://fate.readthedocs.io/en/latest
  • lowreadme#3
    Clarify README's opening sentence to emphasize collaborative model training

    Why:

    CURRENT
    FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy.
    COPY-PASTE FIX
    FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaboratively train AI models while protecting data security and privacy.

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
1 / 2
50% of queries surface FederatedAI/FATE
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
MP-SPDZ
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MP-SPDZ · recommended 2×
  2. TensorFlow Federated (TFF) · recommended 1×
  3. PySyft (OpenMined) · recommended 1×
  4. Flower · recommended 1×
  5. Google's Differential Privacy Library · recommended 1×
  • CATEGORY QUERY
    How can I train machine learning models collaboratively without sharing raw sensitive data?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Federated (TFF)
    2. PySyft (OpenMined)
    3. Flower
    4. Google's Differential Privacy Library
    5. Opacus (PyTorch)
    6. Microsoft SEAL
    7. TenSEAL
    8. MP-SPDZ
    9. FATE (Federated AI Technology Enabler)

    AI recommended 9 alternatives but never named FederatedAI/FATE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source frameworks provide secure multi-party computation for distributed AI model training?
    you: #4
    AI recommended (in order):
    1. PySyft
    2. TF Encrypted
    3. MP-SPDZ
    4. FATE ← you
    5. Conclave
    6. HEuReka
    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 FederatedAI/FATE?
    pass
    AI named FederatedAI/FATE explicitly

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

  • If a team adopts FederatedAI/FATE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FederatedAI/FATE 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 FederatedAI/FATE solve, and who is the primary audience?
    pass
    AI named FederatedAI/FATE explicitly

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

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FederatedAI/FATE — 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