Awesome-FL
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
https://github.com/youngfish42/Awesome-FL
Last synced: 15 days ago
JSON representation
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acknowledgments
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secret sharing
- Awesome-Federated-Learning-on-Graph-and-GNN-papers
- Awesome-Federated-Machine-Learning
- Awesome-Federated-Learning
- awesome-federated-learning
- FederatedAI research
- FLsystem-paper
- Federated Learning Framework Benchmark (UniFed)
- awesome-privacy-chinese
- anomaly-detection-resources
- awesome-image-registration
- Awesome-GNN-Research
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citation
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secret sharing
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-
conference special tracks
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course
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federated learning framework
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benchmark
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table
- PySyft - commit/OpenMined/PySyft) | [A generic framework for privacy preserving deep learning](https://arxiv.org/abs/1811.04017) | [OpenMined](https://www.openmined.org/) | | | [[DOC](https://pysyft.readthedocs.io/en/latest/installing.html)] |
- FATE - commit/FederatedAI/FATE) | [FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection](https://www.jmlr.org/papers/volume22/20-815/20-815.pdf) | [WeBank](https://fedai.org/) | | :white_check_mark::white_check_mark: | [[DOC](https://fate.readthedocs.io/en/latest/)] [[DOC(ZH)](https://fate.readthedocs.io/en/latest/zh/)] |
- Flower - commit/adap/flower) | [Flower: A Friendly Federated Learning Research Framework](https://arxiv.org/abs/2104.03042.pdf) | [flower.ai](https://flower.ai/) | | | [[DOC](https://flower.ai/docs/)] |
- FedML - AI/FedML.svg?color=red)](https://github.com/FedML-AI/FedML/stargazers)<br /> | [FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518) | [FedML](https://fedml.ai/) | :white_check_mark::white_check_mark: | :white_check_mark: | [[DOC](https://doc.fedml.ai/)] |
- SecretFlow - commit/secretflow/secretflow) | | [Ant group](https://www.antgroup.com/) | | :white_check_mark: | [[DOC](https://secretflow.readthedocs.io/en/latest/getting_started/index.html)] |
- PFLlib - commit/TsingZ0/PFLlib) | [PFLlib: Personalized Federated Learning Algorithm Library](https://arxiv.org/abs/2312.04992) | SJTU | | | [[PAGE](http://www.pfllib.com/)] |
- FederatedScope - commit/alibaba/FederatedScope) | [FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://www.vldb.org/pvldb/vol16/p1059-li.pdf) | [Alibaba DAMO Academy](https://damo.alibaba.com/labs/data-analytics-and-intelligence) | :white_check_mark::white_check_mark: | | [[DOC](https://federatedscope.io/refs/index)] [[PAGE](https://federatedscope.io/)] |
- Primihub - commit/primihub/primihub) | | [primihub](https://github.com/primihub) | | | [[DOC]()] |
- Fedlearner - commit/bytedance/fedlearner) | | [Bytedance](https://github.com/bytedance) | | | |
- LEAF - commit/TalwalkarLab/leaf) | [LEAF: A Benchmark for Federated Settings](https://arxiv.org/abs/1812.01097.pdf) | [CMU](https://leaf.cmu.edu/) | | | |
- OpenFL - commit/intel/openfl) | [OpenFL: An open-source framework for Federated Learning](https://arxiv.org/abs/2105.06413) | [Intel](https://github.com/intel) | | | [[DOC](https://openfl.readthedocs.io/en/latest/install.html)] |
- Fedlab - FL/FedLab.svg?color=blue)](https://github.com/SMILELab-FL/FedLab/stargazers)<br /> | [FedLab: A Flexible Federated Learning Framework](https://jmlr.org/papers/v24/22-0440.html) | [SMILELab](https://github.com/SMILELab-FL/) | | | [[DOC](https://fedlab.readthedocs.io/en/master/)] [[DOC(ZH)](https://fedlab.readthedocs.io/zh_CN/latest/)] [[PAGE](https://github.com/SMILELab-FL/FedLab-benchmarks)] |
- NVFlare - commit/NVIDIA/NVFlare) | [NVIDIA FLARE: Federated Learning from Simulation to Real-World](http://sites.computer.org/debull/A23mar/p170.pdf) | [NVIDIA](https://github.com/NVIDIA) | | | [[DOC](https://nvflare.readthedocs.io/en/2.1.1/)] |
- Privacy Meter - commit/privacytrustlab/ml_privacy_meter) | [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://ieeexplore.ieee.org/document/8835245) | University of Massachusetts Amherst | | | |
- NIID-Bench - Computing/NIID-Bench.svg?color=blue)](https://github.com/Xtra-Computing/NIID-Bench/stargazers)<br /> | [Federated Learning on Non-IID Data Silos: An Experimental Study](https://arxiv.org/abs/2102.02079.pdf) | [Xtra Computing Group](https://github.com/Xtra-Computing) | | | |
- FLGo - commit/WwZzz/easyFL) | [Federated Learning with Fair Averaging](https://www.ijcai.org/proceedings/2021/223)<br />[FLGo: A Fully Customizable Federated Learning Platform](https://arxiv.org/abs/2306.12079) | XMU | | | |
- Rosetta - Foundation/Rosetta.svg?color=blue)](https://github.com/LatticeX-Foundation/Rosetta/stargazers)<br /> | | [matrixelements](https://www.matrixelements.com/product/rosetta) | | | [[DOC](https://github.com/LatticeX-Foundation/Rosetta/blob/master/doc/DEPLOYMENT.md)] [[PAGE](https://github.com/LatticeX-Foundation/Rosetta)] |
- PaddleFL - commit/PaddlePaddle/PaddleFL) | | Baidu | | | [[DOC](https://paddlefl.readthedocs.io/en/latest/index.html)] |
- IBM Federated Learning - learning-lib.svg?color=blue)](https://github.com/IBM/federated-learning-lib/stargazers)<br /> | [IBM Federated Learning: an Enterprise Framework White Paper](https://arxiv.org/abs/2007.10987.pdf) | [IBM](https://github.com/IBM) | | :white_check_mark: | [[PAPERS](https://github.com/IBM/federated-learning-lib/blob/main/docs/papers.md)] |
- KubeFATE - commit/FederatedAI/KubeFATE) | | [WeBank](https://fedai.org/) | | | [[WIKI](https://github.com/FederatedAI/KubeFATE/wiki/#faqs)] |
- FedScale - commit/SymbioticLab/FedScale) | [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://arxiv.org/abs/2105.11367.pdf) | [SymbioticLab(U-M)](https://symbioticlab.org/) | | | |
- PersonalizedFL - commit/microsoft/PersonalizedFL) | | microsoft | | | |
- Differentially Private Federated Learning: A Client-level Perspective - samples/machine-learning-diff-private-federated-learning.svg?color=blue)](https://github.comSAP-samples/machine-learning-diff-private-federated-learning/stargazers)<br /> | [Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557) | [SAP-samples](https://github.com/SAP-samples) | | | |
- plato - System/plato.svg?color=blue)](https://github.com/TL-System/plato/stargazers)<br /> | [Plato: An Open-Source Research Framework for Production Federated Learning](https://dl.acm.org/doi/10.1145/3603165.3607364) | UofT | | | |
- Backdoors 101 - commit/ebagdasa/backdoors101) | [Blind Backdoors in Deep Learning Models](https://arxiv.org/abs/2005.03823) | Cornell Tech | | | |
- SWARM LEARNING - learning.svg?color=blue)](https://github.com/HewlettPackard/swarm-learning/stargazers)<br /> | [Swarm Learning for decentralized and confidential clinical machine learning](https://www.nature.com/articles/s41586-021-03583-3) | | | | [[VIDEO](https://github.com/HewlettPackard/swarm-learning/blob/master/docs/videos.md)] |
- EasyFL - AI/EasyFL.svg?color=blue)](https://github.com/EasyFL-AI/EasyFL/stargazers)<br /> | [EasyFL: A Low-code Federated Learning Platform For Dummies](https://ieeexplore.ieee.org/abstract/document/9684558) | NTU | | | |
- Breaching - commit/JonasGeiping/breaching) | A Framework for Attacks against Privacy in Federated Learning ([papers](https://github.com/JonasGeiping/breaching)) | | | | |
- substra - commit/Substra/substra) | | [Substra](https://github.com/Substra) | | | [[DOC](https://doc.substra.ai/index.html)] |
- FedJAX - commit/google/fedjax) | [FEDJAX: Federated learning simulation with JAX](https://arxiv.org/abs/2108.02117.pdf) | [Google](https://ai.googleblog.com/2021/10/fedjax-federated-learning-simulation.html) | | | |
- FLSim - commit/facebookresearch/FLSim) | | [facebook research ](https://github.com/facebookresearch) | | | |
- Galaxy Federated Learning - commit/GalaxyLearning/GFL) | [GFL: A Decentralized Federated Learning Framework Based On Blockchain](https://arxiv.org/abs/2010.10996.pdf) | ZJU | | | [[DOC](http://galaxylearning.github.io/)] |
- FedNLP - AI/FedNLP.svg?color=blue)](https://github.com/FedML-AI/FedNLP/stargazers)<br /> | [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://arxiv.org/abs/2104.08815) | [FedML](https://fedml.ai/) | | | |
- PyVertical - commit/OpenMined/PyVertical) | [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://arxiv.org/abs/2104.00489.pdf) | [OpenMined](https://www.openmined.org/) | | | |
- FLSim - commit/iQua/flsim) | [Optimizing Federated Learning on Non-IID Data with Reinforcement Learning](https://ieeexplore.ieee.org/document/9155494/) | University of Toronto | | | |
- Xaynet - commit/xaynetwork/xaynet) | | [XayNet](https://www.xayn.com/) | | | [[PAGE](https://www.xaynet.dev/)] [[DOC](https://docs.rs/xaynet)] [[WHITEPAPER](https://uploads-ssl.webflow.com/5f0c5c0bb18a279f0a62919e/5f157004da6585f299fa542b_XayNet%20Whitepaper%202.1.pdf)] [[LEGAL REVIEW](https://uploads-ssl.webflow.com/5f0c5c0bb18a279f0a62919e/5fcfa8e3389ecc84a9309513_XAIN%20Legal%20Review%202020%20v1.pdf)] |
- SyferText - commit/OpenMined/SyferText) | | [OpenMined](https://www.openmined.org/) | | | |
- FedTorch - commit/MLOPTPSU/FedTorch) | [Distributionally Robust Federated Averaging](https://papers.nips.cc/paper/2020/file/ac450d10e166657ec8f93a1b65ca1b14-Paper.pdf) | Penn State | | | |
- FLUTE - commit/microsoft/msrflute) | [FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations](https://arxiv.org/abs/2203.13789) | microsoft | | | [[DOC](https://microsoft.github.io/msrflute/)] |
- FedGraphNN - AI/FedGraphNN.svg?color=blue)](https://github.com/FedML-AI/FedGraphNN/stargazers)<br /> | [FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://arxiv.org/abs/2104.07145) | [FedML](https://fedml.ai/) | :white_check_mark::white_check_mark: | | |
- FEDn - commit/scaleoutsystems/fedn) | [Scalable federated machine learning with FEDn](https://ieeexplore.ieee.org/document/9826069/) | [scaleoutsystems](http://www.scaleoutsystems.com) | | | [[DOC](https://scaleoutsystems.github.io/fedn/)] |
- FedTree - Computing/FedTree.svg?color=blue)](https://github.com/Xtra-Computing/FedTree/stargazers)<br /> | [FedTree: A Federated Learning System For Trees](https://proceedings.mlsys.org/paper_files/paper/2023/hash/3430e7055936cb8e26451ed49fce84a6-Abstract-mlsys2023.html) | [Xtra Computing Group](https://github.com/Xtra-Computing) | | :white_check_mark::white_check_mark: | [[DOC](https://fedtree.readthedocs.io/en/latest/index.html)] |
- PhotoLabeller - commit/mccorby/PhotoLabeller) | | | | | [[BLOG](https://proandroiddev.com/federated-learning-e79e054c33ef)] |
- FATE-Serving - Serving.svg?color=blue)](https://github.com/FederatedAI/FATE-Serving/stargazers)<br /> | | [WeBank](https://fedai.org/) | | | [[DOC](https://fate-serving.readthedocs.io/en/develop/)] |
- PriMIA - commit/gkaissis/PriMIA) | [End-to-end privacy preserving deep learning on multi-institutional medical imaging](https://www.nature.com/articles/s42256-021-00337-8) | [TUM](https://www.tum.de/en/); Imperial College London; [OpenMined](https://www.openmined.org) | | | [[DOC](https://g-k.ai/PriMIA/)] |
- APPFL - commit/APPFL/APPFL) | [APPFL: open-source software framework for privacy-preserving federated learning](https://ieeexplore.ieee.org/document/9835407/) | | | | [[DOC](https://appfl.readthedocs.io/en/stable/)] |
- FeTS - AI/Front-End.svg?color=blue)](https://github.com/FETS-AI/Front-End/stargazers)<br /> | [The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research](http://iopscience.iop.org/article/10.1088/1361-6560/ac9449) | [Federated Tumor Segmentation (FeTS) initiative](https://www.med.upenn.edu/cbica/fets/) | | | [[DOC](https://fets-ai.github.io/Front-End/)] |
- FedCV - AI/FedCV.svg?color=blue)](https://github.com/FedML-AI/FedCV/stargazers)<br /> | [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://arxiv.org/abs/2111.11066) | FedML | | | |
- MPLC - learning-contributivity.svg?color=blue)](https://github.com/LabeliaLabs/distributed-learning-contributivity/stargazers)<br /> | | [LabeliaLabs](https://github.com/LabeliaLabs) | | | [[PAGE](https://www.labelia.org)] |
- Flame - open/flame.svg?color=blue)](https://github.com/cisco-open/flame/stargazers)<br /> | [Flame: Simplifying Topology Extension in Federated Learning](https://dl.acm.org/doi/10.1145/3620678.3624665) | Cisco | | | [[DOC](https://fedsim.varnio.com/en/latest/)] |
- FlexCFL - commit/morningD/FlexCFL) | [Flexible Clustered Federated Learning for Client-Level Data Distribution Shift](https://arxiv.org/abs/2108.09749) | Chongqing University | | | |
- FedGroup - commit/morningD/GrouProx) | [FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure](https://arxiv.org/abs/2010.06870) | Chongqing University | | | |
- FedEval - Chai/FedEval.svg?color=blue)](https://github.com/Di-Chai/FedEval/stargazers)<br /> | [FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning](https://arxiv.org/abs/2011.09655) | HKU | | | [[DOC](https://di-chai.github.io/FedEval/)] |
- UCADI - EIC-AI-LAB/UCADI.svg?color=blue)](https://github.com/HUST-EIC-AI-LAB/UCADI/stargazers)<br /> | [Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence](https://www.nature.com/articles/s42256-021-00421-z) | Huazhong University of Science and Technology | | | |
- FedSim - commit/varnio/fedsim) | | | | | |
- GOLF - commit/IntelligentSystemsLab/generic_and_open_learning_federator) | | SYSU | | | [[DOC](https://generic-and-open-learning-federator.readthedocs.io/en/latest/)] |
- Federated-Learning-source - ETH/Federated-Learning-source.svg?color=blue)](https://github.com/MTC-ETH/Federated-Learning-source/stargazers)<br /> | [A Practical Federated Learning Framework for Small Number of Stakeholders](https://dl.acm.org/doi/10.1145/3437963.3441702) | ETH Zürich | | | [[DOC](https://github.com/MTC-ETH/Federated-Learning-source/blob/master/dashboard/README.md)] |
- Clara
- TFF(Tensorflow-Federated) - commit/tensorflow/federated) | [Towards Federated Learning at Scale: System Design](https://proceedings.mlsys.org/paper_files/paper/2019/hash/7b770da633baf74895be22a8807f1a8f-Abstract.html) | Google | | | [[DOC](https://www.tensorflow.org/federated)] [[PAGE](https://www.tensorflow.org/federated)] |
- FedRS-Bench - Bench.svg?color=blue)](https://github.com/dongdongzhaoUP/FedRS-Bench/stargazers)<br /> | [A Practical Federated Learning Framework for realistic Remote Sensing](https://arxiv.org/abs/2505.08325) | SJTU | | | [[DOC](https://github.com/dongdongzhaoUP/FedRS-Bench)] |
- FedLearn - algo.svg?color=blue)](https://github.com/fedlearnAI/fedlearn-algo/stargazers)<br /> | [Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform](https://arxiv.org/abs/2107.04129) | JD | | | |
- OpenFed - commit/FederalLab/OpenFed) | [OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework](https://arxiv.org/abs/2109.07852) | | | | [[DOC](https://openfed.readthedocs.io/README.html)] |
- Federated-Learning-source - ETH/Federated-Learning-source.svg?color=blue)](https://github.com/MTC-ETH/Federated-Learning-source/stargazers)<br /> | [A Practical Federated Learning Framework for Small Number of Stakeholders](https://dl.acm.org/doi/10.1145/3437963.3441702) | ETH Zürich | | | [[DOC](https://github.com/MTC-ETH/Federated-Learning-source/blob/master/dashboard/README.md)] |
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fl datasets
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benchmark
- LEAF
- Federated AI Dataset
- ICML Workshop 2020
- IEEE Commun. Mag. 2020
- China Communications 2020
- Federated Learning Systems - 3-030-70604-3_2)]
- WorldS4 2020
- IEEE Internet Things J. 2022
- IEEE Communications Surveys & Tutorials 2020
- IEEE Communications Surveys & Tutorials 2020
- IEEE Signal Process. Mag. 2020
- IEEE Commun. Mag. 2020
- IEEE TKDE 2021
- IJCAI Workshop 2020
- Foundations and Trends in Machine Learning 2021 - 083)
- IEEE Communications Surveys & Tutorials 2020
- IEEE TIST 2019
- J. Heal. Informatics Res. 2021 - 020-00082-4)
- Ad Hoc Networks 2024 - Privacy-Computing-in-Metaverse)
- Federated-Learning
- ACM Trans. Interact. Intell. Syst.
- 2025
- IEEE Communications Surveys & Tutorials 2020
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fl graph datasets
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benchmark
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fl in top ai conference and journal
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2000
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2007
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2010
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2011
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2012
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2013
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2014
- [PUB - preference-convincing/tree/crowdGPPL)]
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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Categories
fl in top-tier journal
3,291
fl in top ml conference and journal
1,564
fl in top ai conference and journal
753
fl in top cv conference and journal
672
fl on graph data and graph neural networks
432
fl on tabular data
370
fl in top network conference and journal
361
fl in top system conference and journal
332
fl in top dm conference and journal
183
fl in top nlp conference and journal
154
fl in top db conference and journal
137
fl in top secure conference and journal
132
federated learning framework
65
fl in top ir conference and journal
55
workshops
45
fl in top conference and journal other fields
33
fl datasets
28
tutorials
15
acknowledgments
11
course
8
journal special issues
6
update log
5
conference special tracks
3
fl graph datasets
1
citation
1
Sub Categories
Private Graph Neural Networks (todo)
374
2025
336
2026
277
2024
184
2023
108
2021
92
secret sharing
79
2022
78
table
63
2020
47
2019
46
benchmark
46
2018
27
2017
20
2012
16
2015
15
2016
13
1982
13
2014
11
2011
10
2009
9
2013
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2007
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2000
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2006
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2004
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2002
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2005
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2010
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2001
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Keywords
federated-learning
28
machine-learning
18
deep-learning
9
pytorch
8
privacy
6
federated-learning-framework
5
private-set-intersection
3
python
3
non-iid
3
awesome
3
tensorflow
3
privacy-preserving-machine-learning
3
awesome-list
3
papers
3
privacy-preserving
3
privacy-tools
2
gdpr
2
edge-ai
2
psi
2
distributed-learning
2
model-serving
2
mlsys
2
trusted-execution-environment
2
homomorphic-encryption
2
secure-computation
2
secure-multiparty-computation
2
data-privacy
2
distributed
2
decentralized-federated-learning
2
distributed-optimization
2
android
2
edge-computing
2
communication-efficiency
2
federated-analytics
2
fleet-learning
2
security
2
distributed-computing
2
differential-privacy
2
vertical-federated-learning
2
computer-vision
2
scikit-learn
2
inference
2
kotlin
1
neural-networks
1
dl4j
1
optimization
1
research
1
simulator
1
gnn
1
graph
1