mlsanityData to 14 Oct 2026

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

arXiv 2104.10195 ↗First posted 20 Apr 2021eess.IV cs.CVcited by 19

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Abstract (arXiv)

Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. However, non-identical data distribution across clients, known as the non-i.i.d problem in FL, could make this assumption for setting fixed aggregation weights sub-optimal. In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models. We disentangle the parameter set into two parts, local model parameters and global aggregation parameters, and update them iteratively with a communication-efficient algorithm. We first show the validity of our approach by outperforming state-of-the-art FL methods for image recognition on a heterogeneous data split of CIFAR-10. Furthermore, we demonstrate our algorithm's effectiveness on two multi-institutional medical image analysis tasks, i.e., COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 44 of its 54 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

44 of 54 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

19 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  2. 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networkscited by 58
  3. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  4. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  5. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  6. 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
  7. 2022Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentationcited by 16
  8. 2022Federated Multi-organ Segmentation with Inconsistent Labelscited by 15
  9. 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learningcited by 12
  10. 2021FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptationcited by 9
  11. 2022Federated Cross Learning for Medical Image Segmentationcited by 9
  12. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
7 more
  1. 2023Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentationcited by 4
  2. 2022Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Imagescited by 3
  3. 2025Federated nnU-Net for Privacy-Preserving Medical Image Segmentationcited by 3
  4. 2023SplitFed resilience to packet loss: Where to split, that is the questioncited by 2
  5. 2024Optimizing Split Points for Error-Resilient SplitFed Learningcited by 1
  6. 2022FedGraph: an Aggregation Method from Graph Perspectivecited by 0
  7. 2026Auto-FL-Research: Agentic Search for Federated Learning Algorithmscited by 0