RRepoGEO

REPOGEO REPORT · LITE

litian96/FedProx

Default branch master · commit d2a4501f · scanned 6/1/2026, 2:42:56 PM

GitHub: 730 stars · 171 forks

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 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 litian96/FedProx, 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 FedProx's role as an algorithm in distributed ML

    Why:

    CURRENT
    This repository contains the code and experiments for the paper:

> Federated Optimization in Heterogeneous Networks
> 
> MLSys 2020
    COPY-PASTE FIX
    This repository provides the official implementation of FedProx, a robust federated learning algorithm designed to tackle system and statistical heterogeneity in distributed machine learning environments. It contains the code and experiments for our MLSys 2020 paper:

> Federated Optimization in Heterogeneous Networks
> 
> MLSys 2020
  • mediumabout#2
    Add the paper's URL to the repository's homepage field

    Why:

    COPY-PASTE FIX
    https://proceedings.mlsys.org/paper/2020/file/38f5f737577753960711542147ef6000-Paper.pdf
  • lowreadme#3
    Add a concise 'Key Benefits' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Benefits

*   Robust convergence in heterogeneous federated networks.
*   Significantly more stable and accurate convergence behavior relative to FedAvg, improving absolute test accuracy by 22% on average in highly heterogeneous settings.
*   Provides a principled framework to tackle both systems and statistical heterogeneity.

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 litian96/FedProx
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
5%
Of all named tools, what % are you?
Top rival
Ray
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray · recommended 1×
  2. Ray Tune · recommended 1×
  3. Ray Data · recommended 1×
  4. Kubeflow · recommended 1×
  5. Kubeflow Pipelines · recommended 1×
  • CATEGORY QUERY
    How to handle system and data heterogeneity in distributed machine learning environments?
    you: not recommended
    AI recommended (in order):
    1. Ray
    2. Ray Tune
    3. Ray Data
    4. Kubeflow
    5. Kubeflow Pipelines
    6. KFServing
    7. Apache Spark
    8. Spark MLlib
    9. Delta Lake
    10. Dask
    11. Dask-ML
    12. Horovod
    13. Open Federated Learning (OpenFL)

    AI recommended 13 alternatives but never named litian96/FedProx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust federated learning algorithms for achieving stable convergence in heterogeneous networks?
    you: #1
    AI recommended (in order):
    1. FedProx ← you
    2. FedAvgM
    3. SCAFFOLD
    4. FedNova
    5. FedAdam
    6. FedAdagrad
    7. FedYogi
    8. FedOpt
    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 litian96/FedProx?
    pass
    AI named litian96/FedProx explicitly

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

  • If a team adopts litian96/FedProx in production, what risks or prerequisites should they evaluate first?
    pass
    AI named litian96/FedProx 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 litian96/FedProx solve, and who is the primary audience?
    pass
    AI named litian96/FedProx explicitly

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

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litian96/FedProx — 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