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The Flower team is excited to announce the release of Flower 1.15.1 stable, which comes with several quality improvements over 1.15.0. Flower is a friendly framework for collaborative AI and data science. It makes novel approaches such as federated learning, federated evaluation, federated analytics, and fleet learning accessible to a wide audience of researchers and engineers.
Thanks to our contributors
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog order):
Dimitris Stripelis, Heng Pan, Javier, Taner Topal, Yan Gao
What's new?
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Improve time drift compensation in automatic SuperNode authentication (#4899)
In addition to allowing for a time delay (positive time difference), SuperLink now also accounts for time drift, which might result in negative time differences between timestamps in SuperLink and SuperNode during authentication.
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Rename constants for gRPC metadata (#4902)
All metadata keys in gRPC messages that previously used underscores (_) have been replaced with hyphens (-). Using underscores is not recommended in setups where SuperLink may be deployed behind load balancers or reverse proxies.
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Filtering out non-Fleet API requests at the FleetServicer (#4900)
The Fleet API endpoint will now reject gRPC requests that are not part of its API.
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Fix exit handlers mechanism for Windows (#4907)
The SIGQUIT Python signal is not supported on Windows. This signal is now excluded when Flower is executed on Windows.
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Updated Examples (#4895, #4158, #4879)
Examples have been updated to the latest version of Flower. Some examples have also had their dependencies upgraded. The Federated Finetuning of a Whisper model example has been updated to use the new Flower execution method: flwr run.
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Update FlowerTuneLLM Leaderboard evaluation scripts (#4919)
We have updated the package versions used in the evaluation scripts. There is still time to participate in the Flower LLM Leaderboard!
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