Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
Tastan, Nurbek Horváth, Samuel Nandakumar, Karthik
Tastan, Nurbek
Horváth, Samuel
Nandakumar, Karthik
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Collaborative learning enables multiple participants to learn a single global model by exchanging focused updates instead of sharing data. One of the core challenges in collaborative learning is ensuring that participants are rewarded fairly for their contributions, which entails two key sub-problems: contribution assessment and reward allocation. This work focuses on fair reward allocation, where the participants are incentivized through model rewards-differentiated final models whose performance is commensurate with the contribution. In this work, we leverage the concept of slimmable neural networks to collaboratively learn a shared global model whose performance degrades gracefully with a reduction in model width. We also propose a post-training fair allocation algorithm that determines the model width for each participant based on their contributions. We theoretically study the convergence of our proposed approach and empirically validate it using extensive experiments on different datasets and architectures. We also extend our approach to enable training-time model reward allocation. The code can be found at https://github.com/tnurbek/aequa.
Citation
N. Tastan, S. Horváth, and K. Nandakumar, “Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/tastan25a.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
Keywords
Subjects
Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press