mlsanityData to 7 Oct 2026

FedCostWAvg: A new averaging for better Federated Learning

arXiv 2111.08649 ↗First posted 16 Nov 2021cs.LGcited by 11

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Abstract (arXiv)

We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community. Our method addresses the problem of how to aggregate multiple models that were trained on different data sets. Conceptually, we propose a new way to choose the weights when averaging the different models, thereby extending the current state of the art (FedAvg). Empirical validation demonstrates that our approach reaches a notable improvement in segmentation performance compared to FedAvg.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 16 Nov 2021.

Builds on

10 of 19 references.

References · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187
  4. 2020The Future of Digital Health with Federated Learningcited by 625
  5. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  6. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  7. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  8. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  9. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  10. 2021Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)cited by 7

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Cited by year (2026 to 7 Oct)

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

11 papers.

  1. 2023FedPIDAvg: A PID controller inspired aggregation method for Federated Learningcited by 7
  2. 2024Federated brain tumor segmentation: an extensive benchmarkcited by 5
  3. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  4. 2024Combining Federated Learning and Control: A Surveycited by 2
  5. 2022Robust Learning Protocol for Federated Tumor Segmentation Challengecited by 1
  6. 2024FedPID: An Aggregation Method for Federated Learningcited by 1
  7. 2022FedControl: When Control Theory Meets Federated Learningcited by 0
  8. 2022FedGraph: an Aggregation Method from Graph Perspectivecited by 0
  9. 2025FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentationcited by 0
  10. 2025Cooperative SGD with Dynamic Mixing Matricescited by 0
  11. 2025FedPOD: the deployable units of training for federated learningcited by 0