RRepoGEO

REPOGEO REPORT · LITE

FedML-AI/FedML

Default branch master · commit 03e11dfe · scanned 6/29/2026, 4:12:05 PM

GitHub: 4,050 stars · 767 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
70 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify FedML's role as a distributed training and cross-cloud orchestration platform in the README's opening

    Why:

    CURRENT
    # FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale
Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai)
    COPY-PASTE FIX
    # FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale
FEDML is a unified and scalable machine learning library that, with its integrated FEDML Launch scheduler, enables running any AI jobs, including large-scale distributed training and federated learning, across any GPU cloud or on-premise cluster.
Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai)
  • mediumabout#2
    Enhance the repository's 'about' description to explicitly highlight the cross-cloud scheduling and distributed training capabilities

    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 a unified and scalable ML library and cross-cloud scheduler for large-scale distributed training, model serving, and federated learning. Its FEDML Launch component 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.
  • lowtopics#3
    Add topics to explicitly cover cross-cloud orchestration and GPU management

    Why:

    CURRENT
    ai-agent, deep-learning, distributed-training, edge-ai, federated-learning, inference-engine, machine-learning, mlops, model-deployment, model-serving, on-device-training
    COPY-PASTE FIX
    ai-agent, deep-learning, distributed-training, edge-ai, federated-learning, inference-engine, machine-learning, mlops, model-deployment, model-serving, on-device-training, cross-cloud, gpu-orchestration

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 FedML-AI/FedML
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
kubeflow/kubeflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. kubeflow/kubeflow · recommended 1×
  2. Google Kubernetes Engine (GKE) · recommended 1×
  3. Amazon Elastic Kubernetes Service (EKS) · recommended 1×
  4. Azure Kubernetes Service (AKS) · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    How to run large-scale distributed machine learning training across multiple cloud providers?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow (kubeflow/kubeflow)
    2. Google Kubernetes Engine (GKE)
    3. Amazon Elastic Kubernetes Service (EKS)
    4. Azure Kubernetes Service (AKS)
    5. Ray (ray-project/ray)
    6. KubeRay (ray-project/kuberay)
    7. Slurm Workload Manager (SchedMD/slurm)
    8. HTCondor (htcondor/htcondor)
    9. Domino Data Lab
    10. Terraform (hashicorp/terraform)
    11. Pulumi (pulumi/pulumi)
    12. Ansible (ansible/ansible)
    13. Chef (chef/chef)
    14. TensorFlow (tensorflow/tensorflow)
    15. PyTorch (pytorch/pytorch)
    16. Horovod (horovod/horovod)
    17. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 17 alternatives but never named FedML-AI/FedML. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a library to implement federated learning and deploy models on edge devices.
    you: #2
    AI recommended (in order):
    1. Flower
    2. FedML ← you
    3. TensorFlow Federated (TFF)
    4. PySyft
    5. OpenFL
    6. FATE (Federated AI Technology Enabler)
    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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FedML-AI/FedML — RepoGEO report