Announcing Flower 1.12

The Flower Team is excited to announce the release of Flower 1.12 stable and it's packed with updates! 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):

Adam Narozniak, Audris, Charles Beauville, Chong Shen Ng, Daniel J. Beutel, Daniel Nata Nugraha, Heng Pan, Javier, Jiahao Tan, Julian Rußmeyer, Mohammad Naseri, Ray Sun, Robert Steiner, Yan Gao, xiliguguagua

What's new?

• Introduce SuperExec log streaming (#3577, #3584, #4242, #3611, #3613)

  Flower now supports log streaming from a remote SuperExec using the flwr log command. This new feature allows you to monitor logs from SuperExec in real time via flwr log <run-id> (or flwr log <run-id> <app-dir> <federation>).

• Improve flwr new templates (#4291, #4292, #4293, #4294, #4295)

  The flwr new command templates for MLX, NumPy, sklearn, JAX, and PyTorch have been updated to improve usability and consistency across frameworks.

• Migrate ID handling to use unsigned 64-bit integers (#4170, #4237, #4243)

  Node IDs, run IDs, and related fields have been migrated from signed 64-bit integers (sint64) to unsigned 64-bit integers (uint64). To support this change, the uint64 type is fully supported in all communications. You may now use uint64 values in config and metric dictionaries. For Python users, that means using int values larger than the maximum value of sint64 but less than the maximum value of uint64.

• Add Flower architecture explanation (#3270)

  A new Flower architecture explainer page introduces Flower components step-by-step. Check out the EXPLANATIONS section of the Flower documentation if you're interested.

• Introduce FedRep baseline (#3790)

  FedRep is a federated learning algorithm that learns shared data representations across clients while allowing each to maintain personalized local models, balancing collaboration and individual adaptation. Read all the details in the paper: "Exploiting Shared Representations for Personalized Federated Learning" (arxiv)

• Improve FlowerTune template and LLM evaluation pipelines (#4286, #3769, #4272, #4257, #4220, #4282, #4171, #4228, #4258, #4296, #4287, #4217, #4249, #4324, #4219, #4327)

  Refined evaluation pipelines, metrics, and documentation for the upcoming FlowerTune LLM Leaderboard across multiple domains including Finance, Medical, and general NLP. Stay tuned for the official launch—we welcome all federated learning and LLM enthusiasts to participate in this exciting challenge!

• Enhance Docker Support and Documentation (#4191, #4251, #4190, #3928, #4298, #4192, #4136, #4187, #4261, #4177, #4176, #4189, #4297, #4226)

  Upgraded Ubuntu base image to 24.04, added SBOM and gcc to Docker images, and comprehensively updated Docker documentation including quickstart guides and distributed Docker Compose instructions.

• Introduce Flower glossary (#4165, #4235)

  Added the Federated Learning glossary to the Flower repository, located under the flower/glossary/ directory. This resource aims to provide clear definitions and explanations of key FL concepts. Community contributions are highly welcomed to help expand and refine this knowledge base — this is probably the easiest way to become a Flower contributor!

• Implement Message Time-to-Live (TTL) (#3620, #3596, #3615, #3609, #3635)

  Added comprehensive TTL support for messages in Flower's SuperLink. Messages are now automatically expired and cleaned up based on configurable TTL values, available through the low-level API (and used by default in the high-level API).

• Improve FAB handling (#4303, #4264, #4305, #4304)

  An 8-character hash is now appended to the FAB file name. The flwr install command installs FABs with a more flattened folder structure, reducing it from 3 levels to 1.

• Update documentation (#3341, #3338, #3927, #4152, #4151, #3993)

  Updated quickstart tutorials (PyTorch Lightning, TensorFlow, Hugging Face, Fastai) to use the new flwr run command and removed default title from documentation base template. A new blockchain example has been added to FAQ.

• Update example projects (#3716, #4007, #4130, #4234, #4206, #4188, #4247, #4331)

  Refreshed multiple example projects including vertical FL, PyTorch (advanced), Pandas, Secure Aggregation, and XGBoost examples. Optimized Hugging Face quickstart with a smaller language model and removed legacy simulation examples.

• Update translations (#4070, #4316, #4252, #4256, #4210, #4263, #4259)

• General improvements (#4239, 4276, 4204, 4184, 4227, 4183, 4202, 4250, 4267, 4246, 4240, 4265, 4238, 4275, 4318, #4178, #4315, #4241, #4289, #4290, #4181, #4208, #4225, #4314, #4174, #4203, #4274, #3154, #4201, #4268, #4254, #3990, #4212, #2938, #4205, #4222, #4313, #3936, #4278, #4319, #4332, #4333)

  As always, many parts of the Flower framework and quality infrastructure were improved and updated.

Incompatible changes

• Drop Python 3.8 support and update minimum version to 3.9 (#4180, #4213, #4193, #4199, #4196, #4195, #4198, #4194)

  Python 3.8 support was deprecated in Flower 1.9, and this release removes support. Flower now requires Python 3.9 or later (Python 3.11 is recommended). CI and documentation were updated to use Python 3.9 as the minimum supported version. Flower now supports Python 3.9 to 3.12.