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danieljanes committed Oct 11, 2024
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- **Introduce SuperExec log streaming** ([#3577](https://github.com/adap/flower/pull/3577), [#3584](https://github.com/adap/flower/pull/3584), [#4242](https://github.com/adap/flower/pull/4242), [#3611](https://github.com/adap/flower/pull/3611), [#3613](https://github.com/adap/flower/pull/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 by `flwr log <run-ID> <app-dir> <federation>`.
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](https://github.com/adap/flower/pull/4291), [#4292](https://github.com/adap/flower/pull/4292), [#4293](https://github.com/adap/flower/pull/4293), [#4294](https://github.com/adap/flower/pull/4294), [#4295](https://github.com/adap/flower/pull/4295))

The `flwr new` command templates for MLX, NumPy, sklearn, JAX, and torch have been updated to improve usability and consistency across frameworks.
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 Unsigned 64-bit Integers** ([#4170](https://github.com/adap/flower/pull/4170), [#4237](https://github.com/adap/flower/pull/4237), [#4243](https://github.com/adap/flower/pull/4243))
- **Migrate ID handling to use unsigned 64-bit integers** ([#4170](https://github.com/adap/flower/pull/4170), [#4237](https://github.com/adap/flower/pull/4237), [#4243](https://github.com/adap/flower/pull/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`.
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](https://github.com/adap/flower/pull/3270))

A new Flower architecture explainer page with a step-by-step guide has been added to the `EXPLANATIONS` section of the Flower documentation. Feel free to check it out if you're interested!
A new [Flower architecture explainer](https://flower.ai/docs/framework/explanation-flower-architecture.html) page introduces Flower components step-by-step. Check out the `EXPLANATIONS` section of the Flower documentation if you're interested.

- **Introduce FedRep baseline** ([#3790](https://github.com/adap/flower/pull/3790))

FedRep is an FL 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 their paper: "Exploiting Shared Representations for Personalized Federated Learning" ([arxiv](https://arxiv.org/abs/2102.07078))

- **Enhance Dataset Functionality and Documentation** ([#4114](https://github.com/adap/flower/pull/4114), [#4266](https://github.com/adap/flower/pull/4266), [#4260](https://github.com/adap/flower/pull/4260), [#4109](https://github.com/adap/flower/pull/4109), [#4253](https://github.com/adap/flower/pull/4253), [#4111](https://github.com/adap/flower/pull/4111), [#4255](https://github.com/adap/flower/pull/4255), [#4104](https://github.com/adap/flower/pull/4104))

A series of new partitioning features like `IdToSizeFncPartitioner` is introduced to Flower Datasets and a new examples section is added to the docstring for concatenating divisions. Fixed several issues including pathological partitioner behavior with string labels and plotting scale for absolute sizes. Enhanced error messages for incorrect dataset types. Additionally, the Hugging Face Space is embeded directly in the documentation now, offering a more interactive experience—be sure to explore it!
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](https://arxiv.org/abs/2102.07078))

- **Improve FlowerTune template and LLM evaluation pipelines** ([#4286](https://github.com/adap/flower/pull/4286), [#3769](https://github.com/adap/flower/pull/3769), [#4272](https://github.com/adap/flower/pull/4272), [#4257](https://github.com/adap/flower/pull/4257), [#4220](https://github.com/adap/flower/pull/4220), [#4282](https://github.com/adap/flower/pull/4282), [#4171](https://github.com/adap/flower/pull/4171), [#4228](https://github.com/adap/flower/pull/4228), [#4258](https://github.com/adap/flower/pull/4258), [#4296](https://github.com/adap/flower/pull/4296), [#4287](https://github.com/adap/flower/pull/4287), [#4217](https://github.com/adap/flower/pull/4217), [#4249](https://github.com/adap/flower/pull/4249), [#4324](https://github.com/adap/flower/pull/4324), [#4219](https://github.com/adap/flower/pull/4219), [#4327](https://github.com/adap/flower/pull/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 FL enthusiasts to participate in this exciting challenge!
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](https://github.com/adap/flower/pull/4191), [#4251](https://github.com/adap/flower/pull/4251), [#4190](https://github.com/adap/flower/pull/4190), [#3928](https://github.com/adap/flower/pull/3928), [#4298](https://github.com/adap/flower/pull/4298), [#4192](https://github.com/adap/flower/pull/4192), [#4136](https://github.com/adap/flower/pull/4136), [#4187](https://github.com/adap/flower/pull/4187), [#4261](https://github.com/adap/flower/pull/4261), [#4177](https://github.com/adap/flower/pull/4177), [#4176](https://github.com/adap/flower/pull/4176), [#4189](https://github.com/adap/flower/pull/4189), [#4297](https://github.com/adap/flower/pull/4297), [#4226](https://github.com/adap/flower/pull/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.
Upgraded Ubuntu base image to 24.04, added SBOM and gcc to Docker images, and comprehensively updated [Docker documentation](https://flower.ai/docs/framework/docker/index.html) including quickstart guides and distributed Docker Compose instructions.

- **Introduce Flower glossary** ([#4165](https://github.com/adap/flower/pull/4165), [#4235](https://github.com/adap/flower/pull/4235))

Added a Federated Learning glossary in the Flower repository, located under the `flower/glossary/` folder. 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—feel free to check it out!
Added the [Federated Learning glossary](https://flower.ai/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](https://github.com/adap/flower/pull/3620), [#3596](https://github.com/adap/flower/pull/3596), [#3615](https://github.com/adap/flower/pull/3615), [#3609](https://github.com/adap/flower/pull/3609), [#3635](https://github.com/adap/flower/pull/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.
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](https://github.com/adap/flower/pull/4303), [#4264](https://github.com/adap/flower/pull/4264), [#4305](https://github.com/adap/flower/pull/4305), [#4304](https://github.com/adap/flower/pull/4304))

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- **Update example projects** ([#3716](https://github.com/adap/flower/pull/3716), [#4007](https://github.com/adap/flower/pull/4007), [#4130](https://github.com/adap/flower/pull/4130), [#4234](https://github.com/adap/flower/pull/4234), [#4206](https://github.com/adap/flower/pull/4206), [#4188](https://github.com/adap/flower/pull/4188), [#4247](https://github.com/adap/flower/pull/4247))

Refreshed multiple example projects including vertical FL, PyTorc (advanced), Pandas, Secure Aggregation, and XGBoost examples. Optimized Hugging Face quickstart with a smaller language model and removed legacy simulation examples.
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](https://github.com/adap/flower/pull/4070), [#4316](https://github.com/adap/flower/pull/4316), [#4252](https://github.com/adap/flower/pull/4252), [#4256](https://github.com/adap/flower/pull/4256), [#4210](https://github.com/adap/flower/pull/4210), [#4263](https://github.com/adap/flower/pull/4263), [#4259](https://github.com/adap/flower/pull/4259))

- **Bugfixes** ([#4222](https://github.com/adap/flower/pull/4222), [#4313](https://github.com/adap/flower/pull/4313), [#3936](https://github.com/adap/flower/pull/3936), [#4278](https://github.com/adap/flower/pull/4278), [#4319](https://github.com/adap/flower/pull/4319))
- **General improvements** ([#4239](https://github.com/adap/flower/pull/4239), [4276](https://github.com/adap/flower/pull/4276), [4204](https://github.com/adap/flower/pull/4204), [4184](https://github.com/adap/flower/pull/4184), [4227](https://github.com/adap/flower/pull/4227), [4183](https://github.com/adap/flower/pull/4183), [4202](https://github.com/adap/flower/pull/4202), [4250](https://github.com/adap/flower/pull/4250), [4267](https://github.com/adap/flower/pull/4267), [4246](https://github.com/adap/flower/pull/4246), [4240](https://github.com/adap/flower/pull/4240), [4265](https://github.com/adap/flower/pull/4265), [4238](https://github.com/adap/flower/pull/4238), [4275](https://github.com/adap/flower/pull/4275), [4318](https://github.com/adap/flower/pull/4318), [#4178](https://github.com/adap/flower/pull/4178), [#4315](https://github.com/adap/flower/pull/4315), [#4241](https://github.com/adap/flower/pull/4241), [#4289](https://github.com/adap/flower/pull/4289), [#4290](https://github.com/adap/flower/pull/4290), [#4181](https://github.com/adap/flower/pull/4181), [#4208](https://github.com/adap/flower/pull/4208), [#4225](https://github.com/adap/flower/pull/4225), [#4314](https://github.com/adap/flower/pull/4314), [#4174](https://github.com/adap/flower/pull/4174), [#4203](https://github.com/adap/flower/pull/4203), [#4274](https://github.com/adap/flower/pull/4274), [#3154](https://github.com/adap/flower/pull/3154), [#4201](https://github.com/adap/flower/pull/4201), [#4268](https://github.com/adap/flower/pull/4268), [#4254](https://github.com/adap/flower/pull/4254), [#3990](https://github.com/adap/flower/pull/3990), [#4212](https://github.com/adap/flower/pull/4212), [#2938](https://github.com/adap/flower/pull/2938), [#4205](https://github.com/adap/flower/pull/4205), [#4222](https://github.com/adap/flower/pull/4222), [#4313](https://github.com/adap/flower/pull/4313), [#3936](https://github.com/adap/flower/pull/3936), [#4278](https://github.com/adap/flower/pull/4278), [#4319](https://github.com/adap/flower/pull/4319))

- **Improve CI pipeline and development tools** ([#4208](https://github.com/adap/flower/pull/4208), [#4225](https://github.com/adap/flower/pull/4225), [#4314](https://github.com/adap/flower/pull/4314), [#4174](https://github.com/adap/flower/pull/4174), [#4203](https://github.com/adap/flower/pull/4203), [#4274](https://github.com/adap/flower/pull/4274), [#3154](https://github.com/adap/flower/pull/3154), [#4201](https://github.com/adap/flower/pull/4201), [#4268](https://github.com/adap/flower/pull/4268), [#4254](https://github.com/adap/flower/pull/4254), [#3990](https://github.com/adap/flower/pull/3990), [#4212](https://github.com/adap/flower/pull/4212), [#2938](https://github.com/adap/flower/pull/2938), [#4205](https://github.com/adap/flower/pull/4205))

- **General improvements** ([#4239](https://github.com/adap/flower/pull/4239), [4276](https://github.com/adap/flower/pull/4276), [4204](https://github.com/adap/flower/pull/4204), [4184](https://github.com/adap/flower/pull/4184), [4227](https://github.com/adap/flower/pull/4227), [4183](https://github.com/adap/flower/pull/4183), [4202](https://github.com/adap/flower/pull/4202), [4250](https://github.com/adap/flower/pull/4250), [4267](https://github.com/adap/flower/pull/4267), [4246](https://github.com/adap/flower/pull/4246), [4240](https://github.com/adap/flower/pull/4240), [4265](https://github.com/adap/flower/pull/4265), [4238](https://github.com/adap/flower/pull/4238), [4275](https://github.com/adap/flower/pull/4275), [4318](https://github.com/adap/flower/pull/4318), [#4178](https://github.com/adap/flower/pull/4178), [#4315](https://github.com/adap/flower/pull/4315), [#4241](https://github.com/adap/flower/pull/4241), [#4289](https://github.com/adap/flower/pull/4289), [#4290](https://github.com/adap/flower/pull/4290), [#4181](https://github.com/adap/flower/pull/4181))
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](https://github.com/adap/flower/pull/4180), [#4213](https://github.com/adap/flower/pull/4213), [#4193](https://github.com/adap/flower/pull/4193), [#4199](https://github.com/adap/flower/pull/4199), [#4196](https://github.com/adap/flower/pull/4196), [#4195](https://github.com/adap/flower/pull/4195), [#4198](https://github.com/adap/flower/pull/4198), [#4194](https://github.com/adap/flower/pull/4194))
- **Drop Python 3.8 support and update minimum version to 3.9** ([#4180](https://github.com/adap/flower/pull/4180), [#4213](https://github.com/adap/flower/pull/4213), [#4193](https://github.com/adap/flower/pull/4193), [#4199](https://github.com/adap/flower/pull/4199), [#4196](https://github.com/adap/flower/pull/4196), [#4195](https://github.com/adap/flower/pull/4195), [#4198](https://github.com/adap/flower/pull/4198), [#4194](https://github.com/adap/flower/pull/4194))

Dropped support for Python 3.8 across the framework and datasets, updated CI and documentation to use Python 3.9 as the minimum supported version. Flower now supports Python 3.9 to 3.11.
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.

## v1.11.1 (2024-09-11)

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