Data to 7 Oct 2026

Flower: A Friendly Federated Learning Research Framework

arXiv 2007.14390 ↗First posted 28 Jul 2020cs.LG cs.CV stat.MLcited by 449

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Abstract (arXiv)

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.

Key facts. Cited by 449 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 52; 2023: 78; 2024: 99; 2025: 114; 2026: 82.

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2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
2024A Survey of Resource-efficient LLM and Multimodal Foundation Modelscited by 84
2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
2021FedLab: A Flexible Federated Learning Frameworkcited by 54
2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
2022Orchestra: Unsupervised Federated Learning via Globally Consistent Clusteringcited by 45
2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44

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2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 40
2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
2024Advances and Open Challenges in Federated Foundation Modelscited by 33
2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
2022Pretrained Models for Multilingual Federated Learningcited by 30
2024The Future of Large Language Model Pre-training is Federatedcited by 30
2023L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learningcited by 29
2023Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learningcited by 29
2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
2024Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metricscited by 25
2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
2024Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clientscited by 22
2023Phoenix: A Federated Generative Diffusion Modelcited by 21
2022Proportional Fairness in Federated Learningcited by 20
2024C2M3: Cycle-Consistent Multi-Model Mergingcited by 20
2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
2023Decentralized Learning Made Easy with DecentralizePycited by 18
2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
2023Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Uglycited by 17
2021Scalable federated machine learning with FEDncited by 16
2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 16
2024Communication Efficient and Provable Federated Unlearningcited by 16
2022Secure Aggregation for Federated Learning in Flowercited by 15
2022Federated Learning for 5G Base Station Traffic Forecastingcited by 15
2023Hybrid quantum image classification and federated learning for hepatic steatosis diagnosiscited by 15
2024MeanCache: User-Centric Semantic Caching for LLM Web Servicescited by 14
2021On-device Federated Learning with Flowercited by 13

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