mlsanityData to 14 Oct 2026

EasyFL: A Low-code Federated Learning Platform For Dummies

arXiv 2105.07603 ↗First posted 17 May 2021cs.DC cs.AI cs.LGcited by 20

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Abstract (arXiv)

Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method -- Federated Learning (FL). However, these platforms are complex to use and require a deep understanding of FL, which imposes high barriers to entry for beginners, limits the productivity of researchers, and compromises deployment efficiency. In this paper, we propose the first low-code FL platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. We achieve this goal while ensuring great flexibility and extensibility for customization by unifying simple API design, modular design, and granular training flow abstraction. With only a few lines of code, EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. These practical functionalities are heterogeneity simulation, comprehensive tracking, distributed training optimization, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Compared with other platforms, EasyFL not only requires just three lines of code (at least 10x lesser) to build a vanilla FL application but also incurs lower training overhead. Besides, our evaluations demonstrate that EasyFL expedites distributed training by 1.5x. It also improves the efficiency of deployment. We believe that EasyFL will increase the productivity of researchers and democratize FL to wider audiences.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 35 of its 70 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

35 of 70 references.

References · 35

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23 more
  1. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  2. 2020Federated Learning with Matched Averagingcited by 564
  3. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  4. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
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  8. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 4; 2025: 2; 2026: 1.

20 papers.

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  1. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
  2. 2022Divergence-aware Federated Self-Supervised Learningcited by 73
  3. 2021Collaborative Unsupervised Visual Representation Learning from Decentralized Datacited by 69
  4. 2021Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identificationcited by 20
  5. 2023FedWon: Triumphing Multi-domain Federated Learning Without Normalizationcited by 11
  6. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  7. 2022Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysiscited by 9
  8. 2023MAS: Towards Resource-Efficient Federated Multiple-Task Learningcited by 9
  9. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  10. 2023Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMIcited by 6
  11. 2022Protea: Client Profiling within Federated Systems using Flowercited by 4
  12. 2024FLEX: FLEXible Federated Learning Frameworkcited by 4
8 more
  1. 2022WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learningcited by 1
  2. 2022Smart Multi-tenant Federated Learningcited by 1
  3. 2022Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learningcited by 1
  4. 2023VeryFL: A Verify Federated Learning Framework Embedded with Blockchaincited by 1
  5. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  6. 2025FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identificationcited by 0
  7. 2025A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Casecited by 0
  8. 2026Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deploymentcited by 0