securefederatedai/openfederatedlearning

• highreadme#1

  Move the project's archived status and migration note to the very top of the README

  Why:

  CURRENT

  The 'Note: The Open Federated Learning project...' is currently placed after badges and a navigation bar.

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  Move the entire 'Note: The Open Federated Learning project (formerly known as OpenFL) is no longer under active development and will soon be archived. For existing users looking for ongoing support, we recommend the community transitions to Flower framework using the migration guide created in collaboration between our teams.' block to the absolute top of the README, before any other content or links.

  OpenDoneDismiss
• highabout#2

  Update the GitHub repository description to reflect the archived status and migration recommendation

  Why:

  CURRENT

  An Open Framework for Federated Learning.

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  An archived Python framework for Federated Learning. Users are recommended to transition to the Flower framework.

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• mediumtopics#3

  Add topics reflecting the project's archived status and migration guidance

  Why:

  CURRENT

  federated-analytics, federated-evaluation, federated-learning, privacy-preserving-ml, python, trusted-execution-environment

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  federated-analytics, federated-evaluation, federated-learning, privacy-preserving-ml, python, trusted-execution-environment, archived, migration-guide, flower-framework

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I train machine learning models collaboratively while keeping sensitive data private?

  you: not recommended
  AI recommended (in order):

  1. PySyft (OpenMined/PySyft)
  2. TensorFlow Federated (tensorflow/federated)
  3. Flower (adap/flower)
  4. OpenFHE (openfheorg/openfhe)
  5. Microsoft SEAL (microsoft/SEAL)
  6. HElib (homenc/HElib)
  7. Google's Differential Privacy Library (google/differential-privacy)
  8. Opacus (pytorch/opacus)
  9. Graphene-SGX (oscarlab/graphene)
  10. MesaTEE (mesatee/mesatee)

  AI recommended 10 alternatives but never named securefederatedai/openfederatedlearning. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Looking for Python frameworks to implement privacy-preserving distributed machine learning experiments.

  you: not recommended
  AI recommended (in order):

  1. PySyft
  2. TensorFlow Privacy
  3. FATE
  4. OpenFL
  5. CrypTen
  6. Diffprivlib

  AI recommended 6 alternatives but never named securefederatedai/openfederatedlearning. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of securefederatedai/openfederatedlearning?

  pass
  ⚠AI did not name securefederatedai/openfederatedlearning — 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 securefederatedai/openfederatedlearning in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named securefederatedai/openfederatedlearning 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 securefederatedai/openfederatedlearning solve, and who is the primary audience?

  pass
  ⚠AI did not name securefederatedai/openfederatedlearning — 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?