mlsanityData to 7 Oct 2026

FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations

arXiv 2203.13789 ↗First posted 25 Mar 2022cs.LGcited by 27

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Abstract (arXiv)

In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open-source platform for federated learning research and offline simulations. The goal of FLUTE is to enable rapid prototyping and simulation of new federated learning algorithms at scale, including novel optimization, privacy, and communications strategies. We describe the architecture of FLUTE, enabling arbitrary federated modeling schemes to be realized. We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability. A comparison with other established platforms shows speed-ups of up to 42x and savings in memory footprint of 3x. A sample of the platform capabilities is also presented for a range of tasks, as well as other functionality, such as linear scaling for the number of participating clients, and a variety of federated optimizers, including FedAdam, DGA, etcetera.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 39 of its 80 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

39 of 80 references.

References · 39

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2020Adaptive Federated Optimizationcited by 1,036
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2019Large Batch Optimization for Deep Learning: Training BERT in 76 minutescited by 671
  12. 2017Renyi Differential Privacycited by 643
27 more
  1. 2017Large Batch Training of Convolutional Networkscited by 529
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  3. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  4. 2020Adaptive Personalized Federated Learningcited by 353
  5. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  6. 2020The Pushshift Reddit Datasetcited by 289
  7. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 275
  8. 2018Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselinescited by 228
  9. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 199
  10. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  11. 2021Numerical Composition of Differential Privacycited by 174
  12. 2018Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysiscited by 154
  13. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  14. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  15. 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
  16. 2015Asynchronous Parallel Stochastic Gradient for Nonconvex Optimizationcited by 115
  17. 2017Online Deep Learning: Learning Deep Neural Networks on the Flycited by 86
  18. 2021Optimal Accounting of Differential Privacy via Characteristic Functioncited by 83
  19. 2018Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGDcited by 68
  20. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 60
  21. 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
  22. 2019Practical Federated Gradient Boosting Decision Treescited by 53
  23. 2020Generating Representative Headlines for News Storiescited by 39
  24. 2020An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategiescited by 18
  25. 2020Federated Transfer Learning with Dynamic Gradient Aggregationcited by 8
  26. 2021Dynamic Gradient Aggregation for Federated Domain Adaptationcited by 4
  27. 2020Asynchronous Decentralized Learning of a Neural Networkcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 8; 2024: 8; 2025: 2; 2026: 1.

27 papers.

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  1. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  2. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  3. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
  4. 2023FLINT: A Platform for Federated Learning Integrationcited by 17
  5. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  6. 2022Federated Multilingual Models for Medical Transcript Analysiscited by 11
  7. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  8. 2024DrJAX: Scalable and Differentiable MapReduce Primitives in JAXcited by 9
  9. 2023FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Thingscited by 8
  10. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  11. 2022Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learningcited by 7
  12. 2023Flame: Simplifying Topology Extension in Federated Learningcited by 7
15 more
  1. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  2. 2023A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Designcited by 5
  3. 2022Federated Learning with a Sampling Algorithm under Isoperimetrycited by 4
  4. 2022Protea: Client Profiling within Federated Systems using Flowercited by 4
  5. 2022Towards cost-effective and resource-aware aggregation at Edge for Federated Learningcited by 3
  6. 2022UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworkscited by 3
  7. 2024A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networkscited by 3
  8. 2023Project Florida: Federated Learning Made Easycited by 2
  9. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  10. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  11. 2022TorchFL: A Performant Library for Bootstrapping Federated Learning Experimentscited by 0
  12. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  13. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  14. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  15. 2026Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deploymentcited by 0