A Python framework for clustered federated learning and simulation for performance and fairness analysis, based on Flower.
Flower-compatible implementations of clustered FL strategies included:
- Iterative Federated Clustered Algorithm (IFCA) from Ghosh (2020)
- Clustered Federated Learning (CFL) from Sattler (2019)
- Federated Learning with Hierarchical Clustering (FL+HC) from Briggs (2020)
- Weighted Clustered Federated Learning (WeCFL) from Ma (2022)
If you use this package in your work, please cite the paper:
@inproceedings{kyllo2023inflorescence,
title={Inflorescence: A Framework for Evaluating Fairness with Clustered Federated Learning},
author={Kyllo, Alex and Mashhadi, Afra},
booktitle={Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing},
pages={374--380},
year={2023}
}