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Published December 3, 2024 | Version v1.1.0
Software Open

APPFL: Advanced Privacy-Preserving Federated Learning

Authors/Creators

  • 1. Argonne National Laboratory
  • 2. ExxonMobil Technology and Engineering Company
  • 3. University of Illinois at Urbana-Champaign
  • 4. University of California, Santa Cruz
  • 5. University of Cambridge

Description

New Features

  • Support batched MPI, with documentation available here.
  • Add more data readiness metrics such as PCA plot in PR #208
  • Backend support for service.appfl.ai.
  • Add documentation for service.appfl.ai at here.
  • Add logging capabilities to the server side to log the training metadata such as the training and validation losses.
  • Change documentation theme to furo.

Community Standards

Notes

If you use this software, please cite it using the metadata from this file.

Files

APPFL/APPFL-v1.1.0.zip

Files (2.9 MB)

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md5:797fc99485a919d6683b594f2ef0b2e6
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Additional details

Related works

Is supplement to
Software: https://github.com/APPFL/APPFL/tree/v1.1.0 (URL)

Software

Repository URL
https://github.com/APPFL/APPFL