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Bayesian Nonparametric Federated Learning of Neural Networks

arXiv 1905.12022 ↗First posted 28 May 2019stat.ML cs.LGcited by 387

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Abstract (arXiv)

In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data server is assumed to provide local neural network weights, which are modeled through our framework. We then develop an inference approach that allows us to synthesize a more expressive global network without additional supervision, data pooling and with as few as a single communication round. We then demonstrate the efficacy of our approach on federated learning problems simulated from two popular image classification datasets.

Key facts. Cited by 387 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2019.

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Citing papers per year: 2021: 56; 2022: 76; 2023: 91; 2024: 80; 2025: 56; 2026: 28.

387 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 766
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  4. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  5. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  6. 2021A Field Guide to Federated Optimizationcited by 315
  7. 2024Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunitiescited by 254
  8. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  9. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  10. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  11. 2023ZipIt! Merging Models from Different Tasks without Trainingcited by 179
  12. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
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  1. 2022Federated Class-Incremental Learningcited by 127
  2. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  3. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  4. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  5. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 109
  6. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  7. 2023Equivariant Architectures for Learning in Deep Weight Spacescited by 107
  8. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  9. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  10. 2023Deep Model Fusion: A Surveycited by 91
  11. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  12. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 81
  13. 2021FedCM: Federated Learning with Client-level Momentumcited by 77
  14. 2021Personalized Federated Learning with Gaussian Processescited by 76
  15. 2023DELTA: degradation-free fully test-time adaptationcited by 72
  16. 2022On the Importance and Applicability of Pre-Training for Federated Learningcited by 71
  17. 2021Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Datacited by 69
  18. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  19. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  20. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
  21. 2022Re-basin via implicit Sinkhorn differentiationcited by 62
  22. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
  23. 2021FedLab: A Flexible Federated Learning Frameworkcited by 55
  24. 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learningcited by 54
  25. 2023No One Left Behind: Real-World Federated Class-Incremental Learningcited by 53
  26. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  27. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 50
  28. 2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
  29. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 46
  30. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  31. 2021Fed2: Feature-Aligned Federated Learningcited by 42
  32. 2021A Bayesian Federated Learning Framework with Online Laplace Approximationcited by 41
  33. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  34. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  35. 2021RelaySum for Decentralized Deep Learning on Heterogeneous Datacited by 39
  36. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  37. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  38. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38

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