mlsanityData to 7 Oct 2026

Scalable federated machine learning with FEDn

arXiv 2103.00148 ↗First posted 27 Feb 2021cs.LG cs.DCcited by 16

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Abstract (arXiv)

Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic aspects of the problem. However, there is still a lack of federated machine learning frameworks that focus on fundamental aspects such as scalability, robustness, security, and performance in a geographically distributed setting. To bridge this gap we have designed and developed the FEDn framework. A main feature of FEDn is to support both cross-device and cross-silo training settings. This makes FEDn a powerful tool for researching a wide range of machine learning applications in a realistic setting.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 27 Feb 2021.

Builds on

24 of 45 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2016Deep Learning with Differential Privacycited by 2,682
  6. 2018Deep contextualized word representationscited by 2,285
  7. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  8. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  9. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2018LEAF: A Benchmark for Federated Settingscited by 780
  12. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
12 more
  1. 2019Federated Machine Learning: Concept and Applicationscited by 409
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  3. 2017Don't Decay the Learning Rate, Increase the Batch Sizecited by 313
  4. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  5. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  6. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  7. 2018A generic framework for privacy preserving deep learningcited by 103
  8. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  9. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  10. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  11. 2015Strategies and Principles of Distributed Machine Learning on Big Datacited by 16
  12. 2019Revocable Federated Learning: A Benchmark of Federated Forestcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 3; 2025: 2; 2026: 1.

16 papers.

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  1. 2023Accelerating Fair Federated Learning: Adaptive Federated Adamcited by 8
  2. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  3. 2022FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Datacited by 6
  4. 2022FedQAS: Privacy-aware machine reading comprehension with federated learningcited by 3
  5. 2022Towards cost-effective and resource-aware aggregation at Edge for Federated Learningcited by 3
  6. 2023FedCSD: A Federated Learning Based Approach for Code-Smell Detectioncited by 3
  7. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  8. 2023FedBot: Enhancing Privacy in Chatbots with Federated Learningcited by 2
  9. 2023Collaborative Distributed Machine Learningcited by 1
  10. 2025Practical Feasibility of Gradient Inversion Attacks in Federated Learningcited by 1
  11. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  12. 2023Toward efficient resource utilization at edge nodes in federated learningcited by 0
4 more
  1. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  2. 2024Personalized Multi-tier Federated Learningcited by 0
  3. 2025Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Reviewcited by 0
  4. 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0