@inproceedings{
capitaine2024unravelling,
title={Unravelling in Collaborative Learning},
author={Aymeric Capitaine and Etienne Boursier and Antoine Scheid and Eric Moulines and Michael Jordan and El-Mahdi El-Mhamdi and Alain Oliviero Durmus},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JfxqomOs60}
}
Collaborative learning offers a promising avenue for leveraging decentralized data. However, collaboration in groups of strategic learners is not a given. In this work, we consider strategic agents who wish to train a model together but have sampling distributions of different quality. The collaboration is organized by a benevolent aggregator who gathers samples so as to maximize total welfare, but is unaware of data quality. This setting allows us to shed light on the deleterious effect of adverse selection in collaborative learning. More precisely, we demonstrate that when data quality indices are private, the coalition may undergo a phenomenon known as unravelling, wherein it shrinks up to the point that it becomes empty or solely comprised of the worst agent. We show how this issue can be addressed without making use of external transfers, by proposing a novel method inspired by probabilistic verification. This approach makes the grand coalition a Nash equilibrium with high probability  despite information asymmetry, thereby breaking unravelling.
Unravelling in Collaborative Learning
Aymeric Capitaine Etienne Boursier Antoine Scheid Eric Moulines Michael Jordan El-Mahdi El-Mhamdi Alain Oliviero Durmus