# FedCostWAvg: A new averaging for better Federated Learning

arXiv: https://arxiv.org/abs/2111.08649 · HTML: https://mlsanity.com/paper/2111.08649/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [The Federated Tumor Segmentation (FeTS) Challenge](https://mlsanity.com/paper/2105.05874/) (2021)
- [Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)](https://mlsanity.com/paper/2108.13233/) (2021)

## Built on by (most cited first, 11 of 11)

- [FedPIDAvg: A PID controller inspired aggregation method for Federated Learning](https://mlsanity.com/paper/2304.12117/) (2023)
- [Federated brain tumor segmentation: an extensive benchmark](https://mlsanity.com/paper/2410.17265/) (2024)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation](https://mlsanity.com/paper/2603.04887/) (2026)
- [Robust Learning Protocol for Federated Tumor Segmentation Challenge](https://mlsanity.com/paper/2212.08290/) (2022)
- [FedPID: An Aggregation Method for Federated Learning](https://mlsanity.com/paper/2411.02152/) (2024)
- [FedControl: When Control Theory Meets Federated Learning](https://mlsanity.com/paper/2205.14236/) (2022)
- [FedGraph: an Aggregation Method from Graph Perspective](https://mlsanity.com/paper/2210.02733/) (2022)
- [FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation](https://mlsanity.com/paper/2506.22580/) (2025)
- [Cooperative SGD with Dynamic Mixing Matrices](https://mlsanity.com/paper/2508.14565/) (2025)
- [FedPOD: the deployable units of training for federated learning](https://mlsanity.com/paper/2512.20610/) (2025)

## 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.