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https://github.com/tushar-semwal/awesome-federated-computing
:books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.
https://github.com/tushar-semwal/awesome-federated-computing
List: awesome-federated-computing
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:books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.
- Host: GitHub
- URL: https://github.com/tushar-semwal/awesome-federated-computing
- Owner: tushar-semwal
- License: cc0-1.0
- Created: 2019-03-01T10:53:13.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-08-01T04:26:34.000Z (over 1 year ago)
- Last Synced: 2024-05-19T18:05:53.817Z (7 months ago)
- Topics: awesome-list, decentralized, deep-learning, distributed-computing, federated-learning, machine-learning
- Homepage:
- Size: 138 KB
- Stars: 460
- Watchers: 35
- Forks: 84
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesomeai - Federated Computing/Learning
- awesome-ai-awesomeness - Federated Computing/Learning
- awesome-privacy-engineering - awesome-federated-computing
- awesome-machine-learning-resources - **[List - semwal/awesome-federated-computing?style=social) (Table of Contents)
- awesome-Federated-Learning - 4-
- ultimate-awesome - awesome-federated-computing - :books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning. (Other Lists / PowerShell Lists)
README
# Awesome Federated Computing [](https://github.com/sindresorhus/awesome)
A collection of research papers, codes, tutorials and blogs on ML carried out in a federated manner (distributed;decentralized).
**Maintainer** - [Tushar Semwal](https://github.com/tushar-semwal)
*Please feel free to shoot me a PR.😊
## Contents
- [Blogs](#blogs)
- [Survey Papers](#survey-papers)
- [Research Papers](#research-papers)
- [2022](#2022)
- [2021](#2021)
- [2020](#2020)
- [2019](#2019)
- [2018](#2018)
- [2017](#2017)
- [2016](#2016)
- [2015](#2015)
- [Libraries/Frameworks](#librariesframeworks)
- [Tutorials](#tutorials)
- [Datasets](#datasets)
- [Projects](#projects)
## Blogs
* [Learn to adapt Flower for your use-case](https://flower.dev/blog)
* [Online Comic from Google AI on Federated Learning](https://federated.withgoogle.com/)
* [Federated Learning: Collaborative Machine Learning without Centralized Training Data](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html)
* [An Introduction to Federated Learning](http://vision.cloudera.com/an-introduction-to-federated-learning/)
* [Federated learning: Distributed machine learning with data locality and privacy](https://blog.fastforwardlabs.com/2018/11/14/federated-learning.html)
* [Federated Learning: The Future of Distributed Machine Learning](https://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897)
* [Federated Learning for Wake Word Detection](https://medium.com/snips-ai/federated-learning-for-wake-word-detection-c8b8c5cdd2c5)
## Survey Papers
* [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://ieeexplore.ieee.org/document/9780218), IEEE TBD 2022
* [Survey of Personalization Techniques for Federated Learning](https://arxiv.org/pdf/2003.08673.pdf), preprint 2020
* [Advances and Open Problems in Federated Learning](https://hal.inria.fr/hal-02406503/document), HAL-Inria 2019
* [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://arxiv.org/abs/1909.11875), preprint 2019
* [Federated Machine Learning: Concept and Applications](https://dl.acm.org/citation.cfm?id=3298981), ACM TIST 2019
* [Federated Learning: Challenges, Methods, and Future Directions](https://arxiv.org/pdf/1908.07873.pdf), 2019
* [Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf), 2019
* [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://arxiv.org/pdf/1907.08349.pdf), preprint, 2019
## Research Papers
### 2022
* [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://dl.acm.org/doi/abs/10.1145/3542929.3563463), ACM SoCC 2022
### 2021
* [Towards Causal Federated Learning For Enhanced Robustness And Privacy](https://arxiv.org/pdf/2104.06557.pdf), ICLR DPML 2021
* [FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning](https://arxiv.org/abs/2102.02514), preprint
* [OpenFL: An open-source framework for Federated Learning](https://arxiv.org/abs/2105.06413), preprint. [[code](https://github.com/intel/openfl)] 
* [A Bayesian Federated Learning Framework with Multivariate Gaussian Product](https://arxiv.org/abs/2102.01936), preprint
* [Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing](https://ieeexplore.ieee.org/abstract/document/9345723), IEEE Access
* [FedProf: Optimizing Federated Learning with Dynamic Data Profiling](https://arxiv.org/abs/2102.01733), ICML2020
* [Toward Resource-Efficient Federated Learning in Mobile Edge Computing](https://ieeexplore.ieee.org/abstract/document/9354925?casa_token=UUM11Ln9a_MAAAAA:I_t1tncSN1LMKrfFZ1dH-hLma2eTvy68Xw1zYZH-29e9jTTLwK3Eu53q-74-IoeZykALKWXvSg), IEEE Network
* [Scaling Neuroscience Research using Federated Learning](https://arxiv.org/abs/2102.08440), preprint
* [Exploiting Shared Representations for Personalized Federated Learning](https://arxiv.org/abs/2102.07078), preprint
* [FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data](https://www.sciencedirect.com/science/article/abs/pii/S0167739X21000649), FGCS Elsevier
* [Blockchained Federated Learning for Threat Defense](https://arxiv.org/abs/2102.12746)
* [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://arxiv.org/abs/2102.12920), preprint
* [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://arxiv.org/abs/2102.07623), ICLR 2021 [[code](https://github.com/med-air/FedBN)] 
* [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://arxiv.org/abs/2101.02198), preprint
* [FedSim: Similarity guided model aggregation for Federated Learning](https://www.sciencedirect.com/science/article/abs/pii/S0925231221016039), Neurocomputing Journal, [[code](https://github.com/chamathpali/FedSim)] 
### 2020
* [Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data](https://www.nature.com/articles/s41598-020-69250-1), Nature Scientific Reports.
* [Multi-Center Federated Learning](https://arxiv.org/abs/2005.01026), preprint [[code](https://github.com/anonymousgit2020/fedsem)] 
* [Turn Signal Prediction: A Federated Learning Case Study](https://arxiv.org/abs/2012.12401), preprint
* [FedPerf: A Practitioners’ Guide to Performance of Federated Learning Algorithms](https://osf.io/q3vkt), preprint
* [WAFFLe: Weight Anonymized Factorization for Federated Learning](https://arxiv.org/abs/2008.05687), preprint
* [Fed+: A Family of Fusion Algorithms for Federated Learning](https://arxiv.org/abs/2009.06303), preprint
* [Fast-Convergent Federated Learning](https://arxiv.org/pdf/2007.13137.pdf), preprint
* [FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518), preprint [[code](https://github.com/FedML-AI/FedML)] 
* [Federated Visual Classification with Real-World Data Distribution](https://arxiv.org/abs/2003.08082), ECCV 2020 [[code](https://github.com/google-research/google-research/tree/master/federated_vision_datasets)] 
* [Flower: A Friendly Federated Learning Research Framework](https://arxiv.org/abs/2007.14390), preprint [[code](https://flower.dev/)] 
* [Federated Deep Reinforcement Learning for Internet of Things with Decentralized Cooperative Edge Caching](https://doi.org/10.1109/JIOT.2020.2986803), IEEE IoT journal
* [Three Approaches for Personalization with Applications to Federated Learning](https://arxiv.org/abs/2002.10619), preprint
* [Salvaging Federated Learning by Local Adaptation](https://arxiv.org/abs/2002.04758), preprint
* [Federated Learning of a Mixture of Global and Local Models](https://arxiv.org/abs/2002.05516), preprint
* [Federated Learning with Matched Averaging](https://arxiv.org/pdf/2002.06440.pdf), ICLR 2020
* [On the Convergence of FedAvg on Non-IID Data](https://arxiv.org/abs/1907.02189), ICLR 2020. [[code](https://github.com/lx10077/fedavgpy)] 
* [Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning](https://arxiv.org/abs/2003.09603), preprint
* [Knowledge Federation: Hierarchy and Unification](https://arxiv.org/pdf/2002.01647.pdf), preprint
* [Decentralized Knowledge Acquisition for Mobile Internet Applications](https://link.springer.com/article/10.1007/s11280-019-00775-w), World Wide Web, Springer journal
* [Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach](https://ieeexplore.ieee.org/abstract/document/8964354), IEEE Access 2020
* [Multi-Participant Multi-Class Vertical Federated Learning](https://arxiv.org/pdf/2001.11154.pdf), preprint
* [Accelerating DNN Training in Wireless Federated Edge Learning System](https://arxiv.org/pdf/1905.09712.pdf), preprint
* [Federated Optimization in Heterogeneous Networks](https://arxiv.org/abs/1812.06127), MLSYS 2020 [[code](https://github.com/litian96/FedProx)] 
* [A Secure Federated Transfer Learning Framework](https://ieeexplore.ieee.org/document/9076003) IEEE Intelligent Systems 2020
* [Federated Learning for Healthcare Informatics](https://arxiv.org/abs/1911.06270), preprint
### 2019
* [Record and Reward Federated Learning Contributions with Blockchain](https://mblocklab.com/RecordandReward.pdf), IEEE CyberC 2019, [[Code]](https://github.com/sreyafrancis/BlockchainForFederatedLearning) ] 
* [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://arxiv.org/abs/1910.01991), IEEE Transaction on Neural Nets, [[Code](https://github.com/felisat/clustered-federated-learning)] 
* [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://arxiv.org/abs/1903.02891), preprint. [[Code](https://github.com/felisat/federated-learning)] 
* [Federated Learning for Ranking Browser History Suggestions](https://arxiv.org/abs/1911.11807), preprint. [[Code](https://github.com/florian/federated-learning)] 
* [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://arxiv.org/abs/1909.06335), preprint
* [The Non-IID Data Quagmire of Decentralized Machine Learning](https://arxiv.org/abs/1910.00189), preprint
* [Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning](https://arxiv.org/abs/1805.05838), preprint
* [Accelerating DNN Training in Wireless Federated Edge Learning System](https://arxiv.org/abs/1905.09712), preprint
* [FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record](https://arxiv.org/pdf/1811.11400.pdf), NIPS 2018 Workshop
* [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://arxiv.org/pdf/1906.04329.pdf), preprint
* [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://arxiv.org/abs/1901.06455), IEEE RAL 2019
* [Decentralized Federated Learning: A Segmented Gossip Approach](https://arxiv.org/abs/1908.07782), FML 2019
* [Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things](https://ieeexplore.ieee.org/document/8728285), IEEE Access
* [Towards Faster and Better Federated Learning: A Feature Fusion Approach](https://ieeexplore.ieee.org/abstract/document/8803001/), ICIP 2019
* [Decentralized Bayesian Learning over Graphs](https://arxiv.org/pdf/1905.10466.pdf), preprint
* [Federated Multi-task Hierarchical Attention Model for Sensor Analytics](https://arxiv.org/pdf/1905.05142.pdf), preprint
* [FFD: A Federated Learning Based Method for Credit Card Fraud Detection](https://link.springer.com/chapter/10.1007/978-3-030-23551-2_2), International Conference on Big Data 2019.
* [Federated Learning for Time Series Forecasting Using LSTM Networks: Exploiting Similarities Through Clustering](http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1334598&dswid=-6117), Student thesis, KTH
* [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://arxiv.org/abs/1804.05271), IEEE JSAC.
* [Privacy-Preserving Deep Learning via Weight Transmission](https://arxiv.org/abs/1809.03272)
* [Learning Private Neural Language Modeling with Attentive Aggregation](https://arxiv.org/pdf/1812.07108), IJCNN 2019. [[Code](https://github.com/shaoxiongji/fed-att)] 
* [On the Convergence of FedAvg on Non-IID Data](https://arxiv.org/abs/1907.02189), preprint. [[code](https://github.com/lx10077/fedavgpy)] 
* [Federated Learning of Out-of-Vocabulary Words](https://arxiv.org/pdf/1903.10635.pdf)
* [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046)
* [Agnostic Federated Learning](https://arxiv.org/abs/1902.00146) preprint 2019
* [Peer-to-peer Federated Learning on Graphs](https://arxiv.org/abs/1901.11173) preprint
### 2018
* [A Performance Evaluation of Federated Learning Algorithms](https://dl.acm.org/doi/10.1145/3286490.3286559), DIDL 2018
* [How to backdoor federated learning](https://arxiv.org/pdf/1807.00459), preprint
* [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://arxiv.org/abs/1804.08333)
* [Federated Kernelized Multi-Task Learning](http://www.sysml.cc/doc/30.pdf)
* [Federated Learning with Non-IID Data](https://arxiv.org/abs/1806.00582), preprint.
* [Distributed Fine-tuning of Language Models on Private Data](https://openreview.net/pdf?id=HkgNdt26Z), ICLR 2018
* [Federated Learning Based Proactive Content Caching in Edge Computing](https://ieeexplore.ieee.org/abstract/document/8647616/), IEEE GLOBECOM 2018
* [When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning](http://www.commsp.ee.ic.ac.uk/~wiser/dais-ita/tiffany_papers/infocom_2018.pdf), IEEE Infocom 2018
* [How To Backdoor Federated Learning](https://arxiv.org/abs/1807.00459)
* [LEAF: A Benchmark for Federated Settings](https://arxiv.org/abs/1812.01097), preprint. [[code](https://github.com/TalwalkarLab/leaf)] 
* [Federated Learning for Mobile Keyboard Prediction - Gboard](https://arxiv.org/abs/1811.03604)
* [Federated learning of predictive models from federated Electronic Health Records](https://pubmed.ncbi.nlm.nih.gov/29500022/) PMID 2018
### 2017
* [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629), AISTATS 2017
* [Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557), NIPS 2017 Workshop. [[code](https://github.com/SAP/machine-learning-diff-private-federated-learning)] 
* [Federated Tensor Factorization for Computational Phenotyping](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652331/), KDD 2017
* [Federated Multi-Task Learning](http://papers.nips.cc/paper/7029-federated-multi-task-learning.pdf), NIPS 2017
### 2016
* [Practical Secure Aggregation for Federated Learning on User-Held Data](https://arxiv.org/abs/1611.04482), preprint
* [Federated Learning: Strategies for Improving Communication Efficiency](https://arxiv.org/abs/1610.05492), preprint
* [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://arxiv.org/abs/1610.02527), preprint
* [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629), preprint (first FL paper by Google)
### 2015
* [Privacy-Preserving Deep Learning](https://www.comp.nus.edu.sg/~reza/files/Shokri-CCS2015.pdf), ACM SIGSAC 2015
## Libraries/Frameworks
* [PySyft - Github](https://github.com/OpenMined/PySyft) - The PyTorch based library.
* [Tensorflow Federated - TFF](https://www.tensorflow.org/federated) - A library on top of Tensorflow.
* [Industrial Federated Learning Framework](https://github.com/WeBankFinTech/FATE), Federated AI Technology Enabler, WeBank AI
* [PyTorch Federated Learning - Github](https://github.com/shaoxiongji/federated-learning)
* [Paddle Federated Learning](https://github.com/PaddlePaddle/PaddleFL) - Federated Deep Learning in PaddlePaddle.
* [Flower](https://flower.dev/) - A friendly federated learning research framework.
* [OpenFL](https://github.com/intel/openfl) - An open-source framework for Federated Learning on top of TF/PyTorch/etc.
## Tutorials
* [Flower](https://flower.dev/docs/example_walkthrough_pytorch_mnist.html)
* [PySyft](https://github.com/OpenMined/PySyft/tree/dev/examples/tutorials)
* [TFF](https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification)
* [OpenFL](https://github.com/intel/openfl/tree/develop/openfl-tutorials/)
## Datasets
* [LEAF](https://github.com/TalwalkarLab/leaf)
* [Federated iNaturalist/Landmarks](https://github.com/google-research/google-research/tree/master/federated_vision_datasets)
## Projects
* PhotoLabeller by [Jose A. Corbacho](https://github.com/mccorby)
- [Client](https://github.com/mccorby/PhotoLabeller)
- [Server](https://github.com/mccorby/PhotoLabellerServer)
* Ownership Protocol by [Qibing Lee](https://github.com/ownership-labs)
- [DataToken](https://github.com/ownership-labs/DataToken)
- [Compute-to-Data](https://github.com/ownership-labs/Compute-to-Data)