REPOGEO REPORT · LITE

FedML-AI/FedML

Default branch master · commit 03e11dfe · scanned 5/18/2026, 10:07:30 AM

GitHub: 4,044 stars · 765 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 FedML-AI/FedML, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README H1 and opening to emphasize open-source federated learning

Why:

CURRENT

# FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale

COPY-PASTE FIX

# FEDML Open Source: The Unified Library for Federated Learning and Distributed AI at Scale

FEDML is an open-source, unified, and scalable machine learning library specifically designed for federated learning, distributed training, and on-device AI deployment.

mediumabout#2

Clarify the 'About' description to prioritize the open-source library

Why:

CURRENT

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.

COPY-PASTE FIX

FEDML is an open-source, unified, and scalable machine learning library for federated learning, distributed training, and on-device AI. It supports large-scale model serving and deployment. FEDML Launch, a cross-cloud scheduler, enables running AI jobs on any GPU cloud or on-premise cluster. TensorOpera AI (https://TensorOpera.ai) is a generative AI platform built on this library.

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 FedML-AI/FedML

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

kubeflow/kubeflow

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

kubeflow/kubeflow · recommended 1×
Google Cloud Vertex AI · recommended 1×
Azure Machine Learning · recommended 1×
AWS SageMaker · recommended 1×
mlflow/mlflow · recommended 1×

CATEGORY QUERY
How to run large-scale distributed machine learning training and deployment across multiple clouds?
you: not recommended
AI recommended (in order):
1. Kubeflow (kubeflow/kubeflow)
2. Google Cloud Vertex AI
3. Azure Machine Learning
4. AWS SageMaker
5. MLflow (mlflow/mlflow)
6. Ray (ray-project/ray)
AI recommended 6 alternatives but never named FedML-AI/FedML. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are options for secure federated learning and on-device AI model training at scale?
you: not recommended
AI recommended (in order):
1. TensorFlow Federated (TFF)
2. PySyft (OpenMined)
3. Flower
4. Intel OpenFL (Federated Learning Library)
5. IBM Federated Learning
6. NVIDIA FLARE (Federated Learning Application Runtime Environment)
AI recommended 6 alternatives but never named FedML-AI/FedML. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
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 FedML-AI/FedML?
pass
AI named FedML-AI/FedML explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts FedML-AI/FedML in production, what risks or prerequisites should they evaluate first?
pass
AI named FedML-AI/FedML 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 FedML-AI/FedML solve, and who is the primary audience?
pass
AI named FedML-AI/FedML explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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FedML-AI/FedML — 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