RRepoGEO

REPOGEO REPORT · LITE

lokinko/Federated-Learning

Default branch main · commit b98ec5ca · scanned 5/15/2026, 1:38:18 AM

GitHub: 1,150 stars · 203 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 lokinko/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
  • highreadme#1
    Reposition README opening to clarify repo's purpose as a paper collection

    Why:

    CURRENT
    # Federated Learning
    COPY-PASTE FIX
    # Federated Learning: A Curated Collection of Papers and Surveys

This repository serves as a comprehensive resource, compiling key papers and surveys on Federated Learning, its challenges, methods, and future directions. It is intended for researchers and practitioners seeking to understand the landscape of decentralized and privacy-preserving machine learning.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    federated-learning, machine-learning, privacy-preserving-ml, distributed-ml, research, papers, survey, academic-papers
  • highlicense#3
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory of the repository. For a collection of academic resources, consider a Creative Commons license like CC-BY-4.0 for the collection itself, or a standard open-source license like MIT if the collection includes code snippets or tools.

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 lokinko/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
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/federated · recommended 2×
  2. OpenMined/PySyft · recommended 2×
  3. microsoft/SEAL · recommended 2×
  4. OpenMined/TenSEAL · recommended 2×
  5. data61/MP-SPDZ · recommended 2×
  • CATEGORY QUERY
    How can I implement collaborative machine learning while keeping data decentralized?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Federated (tensorflow/federated)
    2. PySyft (OpenMined/PySyft)
    3. Microsoft SEAL (microsoft/SEAL)
    4. TenSEAL (OpenMined/TenSEAL)
    5. Concrete (zama-ai/concrete)
    6. MP-SPDZ (data61/MP-SPDZ)
    7. Sharemind
    8. Google's Differential Privacy Library (google/differential-privacy)
    9. Opacus (pytorch/opacus)
    10. Ocean Protocol
    11. Fetch.ai

    AI recommended 11 alternatives but never named lokinko/Federated-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best approaches for privacy-preserving machine learning across multiple organizations?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Federated (TFF) (tensorflow/federated)
    2. PySyft (OpenMined) (OpenMined/PySyft)
    3. Flower (adap/flower)
    4. Microsoft SEAL (microsoft/SEAL)
    5. HElib (shaih/HElib)
    6. TenSEAL (OpenMined/TenSEAL)
    7. MP-SPDZ (data61/MP-SPDZ)
    8. Sharemind
    9. Opacus (Meta AI) (pytorch/opacus)
    10. TensorFlow Privacy (tensorflow/privacy)
    11. SmartNoise (OpenMined/Harvard) (opendp/smartnoise-sdk)
    12. Intel SGX (Software Guard Extensions)
    13. AMD SEV (Secure Encrypted Virtualization)

    AI recommended 13 alternatives but never named lokinko/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
    fail

    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 lokinko/Federated-Learning?
    pass
    AI did not name lokinko/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 lokinko/Federated-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lokinko/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 lokinko/Federated-Learning solve, and who is the primary audience?
    pass
    AI named lokinko/Federated-Learning explicitly

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

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lokinko/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