mlsanityData to 14 Oct 2026

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

arXiv 1905.06731 ↗First posted 16 May 2019cs.LG stat.MLcited by 117

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Abstract (arXiv)

Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult for an individual medical center to reach large enough sample sizes to build their own, personalized models. As an alternative, data from all centers could be pooled to train a centralized model that everyone can use. However, such a strategy is often infeasible due to the privacy-sensitive nature of medical data. Recently, federated learning (FL) has been introduced to collaboratively learn a shared prediction model across centers without the need for sharing data. In FL, clients are locally training models on site-specific datasets for a few epochs and then sharing their model weights with a central server, which orchestrates the overall training process. Importantly, the sharing of models does not compromise patient privacy. A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients. In this paper, we introduce BrainTorrent, a new FL framework without a central server, particularly targeted towards medical applications. BrainTorrent presents a highly dynamic peer-to-peer environment, where all centers directly interact with each other without depending on a central body. We demonstrate the overall effectiveness of FL for the challenging task of whole brain segmentation and observe that the proposed server-less BrainTorrent approach does not only outperform the traditional server-based one but reaches a similar performance to a model trained on pooled data.

Key facts. Cited by 117 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2019.

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Citing papers per year: 2021: 23; 2022: 21; 2023: 30; 2024: 22; 2025: 14; 2026: 7.

117 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  3. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 134
  4. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  5. 2021Federated Quantum Machine Learningcited by 82
  6. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  7. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  8. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  9. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  10. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  11. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 37
  12. 2021PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakagecited by 28
38 more
  1. 2023A Survey on Decentralized Federated Learningcited by 27
  2. 2021IPLS : A Framework for Decentralized Federated Learningcited by 26
  3. 2021Decentralized federated learning of deep neural networks on non-iid datacited by 25
  4. 2022A Survey on Federated Recommendation Systemscited by 25
  5. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  6. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
  7. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  8. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 22
  9. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  10. 2024Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchorscited by 21
  11. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  12. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  13. 2023Transformers in Healthcare: A Surveycited by 17
  14. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  15. 2023Fedstellar: A Platform for Decentralized Federated Learningcited by 14
  16. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 13
  17. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  18. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  19. 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11
  20. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  21. 2022Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Casecited by 11
  22. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  23. 2021Scaling Neuroscience Research using Federated Learningcited by 10
  24. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  25. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysiscited by 9
  26. 2023Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Trainingcited by 8
  27. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  28. 2021FedDropoutAvg: Generalizable federated learning for histopathology image classificationcited by 7
  29. 2023The effect of network topologies on fully decentralized learning: a preliminary investigationcited by 7
  30. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 7
  31. 2024Impact of network topology on the performance of Decentralized Federated Learningcited by 7
  32. 2022Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclientscited by 6
  33. 2023Multi-Source to Multi-Target Decentralized Federated Domain Adaptationcited by 6
  34. 2021Generation of Synthetic Electronic Health Records Using a Federated GANcited by 5
  35. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  36. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5
  37. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  38. 2022Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Settingcited by 4

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