# FedCostWAvg: A new averaging for better Federated Learning

**Authors:** Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Benedikt Wiestler, Bjoern Menze  
**arXiv:** [2111.08649](https://arxiv.org/abs/2111.08649)  
**Date:** 2021-11-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2111.08649v1/

## Key points

- The paper presents a novel aggregation strategy called FedCostWAvg for federated learning
- FedCostWAvg achieved success in the MICCAI Federated Tumor Segmentation Challenge 2021
- The authors address the problem of aggregating multiple models trained on different datasets
- FedCostWAvg is an extension of the current state-of-the-art technique known as FedAvg
- Empirical validation shows significant improvements in segmentation performance compared to FedAvg
- FedCostWAvg offers a simple yet effective solution to enhance federated learning outcomes
- The approach tackles the critical issue of combining models trained on diverse data sources
- The authors include Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Benedikt Wiestler, and Bjoern Menze
- Their work showcases their expertise and contributions to advancing federated learning techniques
- This research introduces an innovative approach that has proven successful in a competitive setting and holds promise for further advancements in federated learning applications.

## Summary

The paper titled "FedCostWAvg: A new averaging for better Federated Learning" presents a novel aggregation strategy for federated learning that achieved success in the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS). This challenge marked the first-ever competition on Federated Learning in the Machine Learning community. The authors address the problem of aggregating multiple models trained on different datasets, proposing a new approach to selecting weights during model averaging. This method builds upon the current state-of-the-art technique known as FedAvg. The proposed aggregation strategy, called FedCostWAvg, demonstrates empirical validation showing significant improvements in segmentation performance compared to FedAvg. By extending the existing methodology, FedCostWAvg offers a simple yet effective solution to enhance federated learning outcomes. The authors' approach tackles the critical issue of combining models trained on diverse data sources and provides a more accurate and robust segmentation performance. The authors of this paper include Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Benedikt Wiestler, and Bjoern Menze. Their work showcases their expertise and contributions to advancing federated learning techniques. Overall, this research introduces an innovative approach that has proven successful in a competitive setting and holds promise for further advancements in federated learning applications.