Data to 7 Oct 2026

FedML: A Research Library and Benchmark for Federated Machine Learning

arXiv 2007.13518 ↗First posted 27 Jul 2020cs.LG stat.MLcited by 298

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Abstract (arXiv)

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.

Key facts. Cited by 298 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 59; 2022: 58; 2023: 78; 2024: 56; 2025: 30; 2026: 17.

298 papers.

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2021Model-Contrastive Federated Learningcited by 762
2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
2021Federated Learning on Non-IID Data: A Surveycited by 345
2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
2021Trustworthy AI: From Principles to Practicescited by 206
2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 140
2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137

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2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
2021Trustworthy AI: A Computational Perspectivecited by 93
2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
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
2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
2021Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Datacited by 69
2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
2021FedLab: A Flexible Federated Learning Frameworkcited by 54
2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
2021SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Trainingcited by 45
2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
2022Test-Time Robust Personalization for Federated Learningcited by 39
2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
2021Layer-wise Adaptive Model Aggregation for Scalable Federated Learningcited by 35
2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 35
2022Federated Learning under Distributed Concept Driftcited by 35
2024Low-Rank Adaptation for Foundation Models: A Comprehensive Reviewcited by 35
2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
2024Advances and Open Challenges in Federated Foundation Modelscited by 33

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