TN
tnurbek/aequa
[ICML 2025] Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks [ICML 2025]
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks [ICML 2025]
Nurbek Tastan, Samuel Horvath, Karthik Nandakumar
Dependencies
pip install -r requirements.txtRun Aequa
Default dataset is CIFAR-10 with custom CNN.
python3 main_aequa.py -T 100 -S homogeneous -model_arch cnn -lr 0.01 To test (to obtain width-wise performance measures), simply run main_test.py file with the same configurations from the training above:
python3 main_test.py -T 100 -S homogeneous -model_arch cnn -lr 0.01 Citation
If you like the work, please consider citing us and explore other works:
@inproceedings{tastan2025aequa,
title = {{Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks}},
author = {Tastan, Nurbek and Horv\'{a}th, Samuel and Nandakumar, Karthik},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
pages = {59210--59236},
year = {2025},
editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry},
volume = {267},
series = {Proceedings of Machine Learning Research},
month = {13--19 Jul},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/tastan25a/tastan25a.pdf},
url = {https://proceedings.mlr.press/v267/tastan25a.html}
}
@article{tastan2025cycle,
title = {{{CYC}le: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning}},
author = {Nurbek Tastan and Samuel Horv{\'a}th and Karthik Nandakumar},
journal = {Transactions on Machine Learning Research},
issn = {2835-8856},
year = {2025},
url = {https://openreview.net/forum?id=ygqNiLQqfH},
note = {}
}
@inproceedings{tastan2024redefining,
title = {{Redefining Contributions: Shapley-Driven Federated Learning}},
author = {Tastan, Nurbek and Fares, Samar and Aremu, Toluwani and Horváth, Samuel and Nandakumar, Karthik},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {5009--5017},
year = {2024},
month = {8},
note = {Main Track},
}Acknowledgements
We would like to thank IAFL repository for open-sourcing their code.