[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9763":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":45,"readmeContent":46,"aiSummary":47,"trendingCount":16,"starSnapshotCount":16,"syncStatus":48,"lastSyncTime":49,"discoverSource":50},9763,"flower","flwrlabs\u002Fflower","flwrlabs","Flower: A Friendly Federated AI Framework","https:\u002F\u002Fflower.ai",null,"Python",6937,1199,42,31,0,1,10,56,8,40.24,"Apache License 2.0",false,"main",[26,27,28,29,30,31,32,33,34,35,5,36,37,38,39,40,41,42,43,44],"ai","android","artificial-intelligence","cpp","deep-learning","federated-analytics","federated-learning","federated-learning-framework","fleet-intelligence","fleet-learning","framework","grpc","ios","machine-learning","python","pytorch","raspberry-pi","scikit-learn","tensorflow","2026-06-12 02:02:12","# Flower: A Friendly Federated AI Framework\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fflower.ai\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fflower.ai\u002Fstatic\u002Fimages\u002Ficon\u002Ficon.png\" width=\"140px\" alt=\"Flower Website\" \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fflower.ai\u002F\">Website\u003C\u002Fa> |\n    \u003Ca href=\"https:\u002F\u002Fflower.ai\u002Fblog\">Blog\u003C\u002Fa> |\n    \u003Ca href=\"https:\u002F\u002Fflower.ai\u002Fdocs\u002F\">Docs\u003C\u002Fa> |\n    \u003Ca href=\"https:\u002F\u002Fflower.ai\u002Fjoin-slack\">Slack\u003C\u002Fa>\n    \u003Cbr \u002F>\u003Cbr \u002F>\n\u003C\u002Fp>\n\n[![GitHub license](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fflwrlabs\u002Fflower)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Fblob\u002Fmain\u002FLICENSE)\n[![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Fblob\u002Fmain\u002FCONTRIBUTING.md)\n![Build](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Factions\u002Fworkflows\u002Fframework.yml\u002Fbadge.svg)\n[![Downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fflwr)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fflwr)\n[![Docker Hub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDocker%20Hub-flwr-blue)](https:\u002F\u002Fhub.docker.com\u002Fu\u002Fflwr)\n[![Slack](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FChat-Slack-red)](https:\u002F\u002Fflower.ai\u002Fjoin-slack)\n\nFlower (`flwr`) is a framework for building federated AI systems. The\ndesign of Flower is based on a few guiding principles:\n\n- **Customizable**: Federated learning systems vary wildly from one use case to\n  another. Flower allows for a wide range of different configurations depending\n  on the needs of each individual use case.\n\n- **Extendable**: Flower originated from a research project at the University of\n  Oxford, so it was built with AI research in mind. Many components can be\n  extended and overridden to build new state-of-the-art systems.\n\n- **Framework-agnostic**: Different machine learning frameworks have different\n  strengths. Flower can be used with any machine learning framework, for\n  example, [PyTorch](https:\u002F\u002Fpytorch.org), [TensorFlow](https:\u002F\u002Ftensorflow.org), [Hugging Face Transformers](https:\u002F\u002Fhuggingface.co\u002F), [PyTorch Lightning](https:\u002F\u002Fpytorchlightning.ai\u002F), [scikit-learn](https:\u002F\u002Fscikit-learn.org\u002F), [JAX](https:\u002F\u002Fjax.readthedocs.io\u002F), [TFLite](https:\u002F\u002Ftensorflow.org\u002Flite\u002F), [MONAI](https:\u002F\u002Fdocs.monai.io\u002Fen\u002Flatest\u002Findex.html), [fastai](https:\u002F\u002Fwww.fast.ai\u002F), [MLX](https:\u002F\u002Fml-explore.github.io\u002Fmlx\u002Fbuild\u002Fhtml\u002Findex.html), [XGBoost](https:\u002F\u002Fxgboost.readthedocs.io\u002Fen\u002Fstable\u002F), [CatBoost](https:\u002F\u002Fcatboost.ai\u002F), [LeRobot](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Flerobot) for federated robots, [Pandas](https:\u002F\u002Fpandas.pydata.org\u002F) for federated analytics, or even raw [NumPy](https:\u002F\u002Fnumpy.org\u002F)\n  for users who enjoy computing gradients by hand.\n\n- **Understandable**: Flower is written with maintainability in mind. The\n  community is encouraged to both read and contribute to the codebase.\n\nMeet the Flower community on [flower.ai](https:\u002F\u002Fflower.ai)!\n\n## Federated Learning Tutorial\n\nFlower's goal is to make federated learning accessible to everyone. This series of tutorials introduces the fundamentals of federated learning and how to implement them in Flower.\n\n0. **[What is Federated Learning?](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Fmain\u002Fen\u002Ftutorial-series-what-is-federated-learning.html)**\n\n1. **[An Introduction to Federated Learning](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Fmain\u002Fen\u002Ftutorial-series-get-started-with-flower-pytorch.html)**\n\n2. **[Using Strategies in Federated Learning](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Fmain\u002Fen\u002Ftutorial-series-use-a-federated-learning-strategy-pytorch.html)**\n\n3. **[Customize a Flower Strategy](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Fmain\u002Fen\u002Ftutorial-series-build-a-strategy-from-scratch-pytorch.html)**\n\n4. **[Communicate Custom Messages](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Fmain\u002Fen\u002Ftutorial-series-customize-the-client-pytorch.html)**\n\nStay tuned, more tutorials are coming soon. Topics include **Privacy and Security in Federated Learning**, and **Scaling Federated Learning**.\n\n## Documentation\n\n[Flower Docs](https:\u002F\u002Fflower.ai\u002Fdocs):\n\n- [Installation](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Fhow-to-install-flower.html)\n- [Quickstart (TensorFlow)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-tensorflow.html)\n- [Quickstart (PyTorch)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-pytorch.html)\n- [Quickstart (Hugging Face)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-huggingface.html)\n- [Quickstart (PyTorch Lightning)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-pytorch-lightning.html)\n- [Quickstart (Pandas)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-pandas.html)\n- [Quickstart (fastai)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-fastai.html)\n- [Quickstart (JAX)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-jax.html)\n- [Quickstart (scikit-learn)](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-scikitlearn.html)\n- [Quickstart (Android [TFLite])](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-android.html)\n- [Quickstart (iOS [CoreML])](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fframework\u002Ftutorial-quickstart-ios.html)\n\n## Flower Baselines\n\nFlower Baselines is a collection of community-contributed projects that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas. The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline!\n\n- [DASHA](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fdasha)\n- [DepthFL](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fdepthfl)\n- [FedBN](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedbn)\n- [FedMeta](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedmeta)\n- [FedMLB](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedmlb)\n- [FedPer](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedper)\n- [FedProx](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedprox)\n- [FedNova](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffednova)\n- [HeteroFL](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fheterofl)\n- [FedAvgM](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedavgm)\n- [FedRep](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedrep)\n- [FedStar](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedstar)\n- [FedWav2vec2](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedwav2vec2)\n- [FjORD](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffjord)\n- [MOON](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fmoon)\n- [niid-Bench](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fniid_bench)\n- [TAMUNA](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ftamuna)\n- [FedVSSL](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedvssl)\n- [FedXGBoost](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fhfedxgboost)\n- [FedPara](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Ffedpara)\n- [FedAvg](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fflwr_baselines\u002Fflwr_baselines\u002Fpublications\u002Ffedavg_mnist)\n- [FedOpt](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fbaselines\u002Fflwr_baselines\u002Fflwr_baselines\u002Fpublications\u002Fadaptive_federated_optimization)\n\nPlease refer to the [Flower Baselines Documentation](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fbaselines\u002F) for a detailed categorization of baselines and for additional info including:\n* [How to use Flower Baselines](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fbaselines\u002Fhow-to-use-baselines.html)\n* [How to contribute a new Flower Baseline](https:\u002F\u002Fflower.ai\u002Fdocs\u002Fbaselines\u002Fhow-to-contribute-baselines.html)\n\n## Flower Usage Examples\n\nSeveral code examples show different usage scenarios of Flower (in combination with popular machine learning frameworks such as PyTorch or TensorFlow).\n\nQuickstart examples:\n\n- [Quickstart (TensorFlow)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-tensorflow)\n- [Quickstart (PyTorch)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-pytorch)\n- [Quickstart (Hugging Face)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-huggingface)\n- [Quickstart (PyTorch Lightning)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-pytorch-lightning)\n- [Quickstart (fastai)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-fastai)\n- [Quickstart (Pandas)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-pandas)\n- [Quickstart (JAX)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-jax)\n- [Quickstart (MONAI)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-monai)\n- [Quickstart (scikit-learn)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-sklearn)\n- [Quickstart (Android [TFLite])](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fandroid)\n- [Quickstart (iOS [CoreML])](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fios)\n- [Quickstart (MLX)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-mlx)\n- [Quickstart (XGBoost)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fxgboost-quickstart)\n- [Quickstart (CatBoost)](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fquickstart-catboost)\n\nOther [examples](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples):\n\n- [Raspberry Pi & Nvidia Jetson Tutorial](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fembedded-devices)\n- [PyTorch: From Centralized to Federated](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fpytorch-from-centralized-to-federated)\n- [Vertical FL](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fvertical-fl)\n- [Federated Finetuning of OpenAI's Whisper](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fwhisper-federated-finetuning)\n- [Federated Finetuning of Large Language Model](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fflowertune-llm)\n- [Federated Finetuning of a Vision Transformer](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fflowertune-vit)\n- [Advanced Flower with PyTorch](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fadvanced-pytorch)\n- [Comprehensive Flower+XGBoost](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fxgboost-comprehensive)\n- [Flower with KaplanMeierFitter from the lifelines library](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Ffederated-kaplan-meier-fitter)\n- [Sample Level Privacy with Opacus](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Fopacus)\n- [Flower with a Tabular Dataset](https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Ftree\u002Fmain\u002Fexamples\u002Ffl-tabular)\n\n## Community\n\nFlower is built by a wonderful community of researchers and engineers. [Join Slack](https:\u002F\u002Fflower.ai\u002Fjoin-slack) to meet them, [contributions](#contributing-to-flower) are welcome.\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fflwrlabs\u002Fflower\u002Fgraphs\u002Fcontributors\">\n  \u003Cimg src=\"https:\u002F\u002Fcontrib.rocks\u002Fimage?repo=flwrlabs\u002Fflower&columns=10\" \u002F>\n\u003C\u002Fa>\n\n## Citation\n\nIf you publish work that uses Flower, please cite Flower as follows:\n\n```bibtex\n@article{beutel2020flower,\n  title={Flower: A Friendly Federated Learning Research Framework},\n  author={Beutel, Daniel J and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Kwing, Hei Li and Parcollet, Titouan and Gusmão, Pedro PB de and Lane, Nicholas D},\n  journal={arXiv preprint arXiv:2007.14390},\n  year={2020}\n}\n```\n\nPlease also consider adding your publication to the list of Flower-based publications in the docs, just open a Pull Request.\n\n## Contributing to Flower\n\nWe welcome contributions. Please see [CONTRIBUTING.md](CONTRIBUTING.md) to get started!\n","Flower 是一个用于构建联邦AI系统的友好框架。其核心功能包括高度可定制化和扩展性，支持多种机器学习框架如PyTorch、TensorFlow等，并且设计上注重代码的可读性和维护性。技术特点方面，Flower 允许用户根据具体需求灵活配置系统，同时提供丰富的组件供研究者开发最先进的联邦学习模型。它适用于需要保护数据隐私的同时进行模型训练的各种场景，比如医疗健康数据分析、移动设备上的个性化推荐等。",2,"2026-06-11 03:24:37","top_topic"]