Unravelling in Collaborative Learning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909424114728960 |
|---|---|
| author | Capitaine, Aymeric Boursier, Etienne Scheid, Antoine Moulines, Eric Jordan, Michael I. El-Mhamdi, El-Mahdi Durmus, Alain |
| author_facet | Capitaine, Aymeric Boursier, Etienne Scheid, Antoine Moulines, Eric Jordan, Michael I. El-Mhamdi, El-Mahdi Durmus, Alain |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unravelling in Collaborative Learning Capitaine, Aymeric Boursier, Etienne Scheid, Antoine Moulines, Eric Jordan, Michael I. El-Mhamdi, El-Mahdi Durmus, Alain Computer Science and Game Theory 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. |
| title | Unravelling in Collaborative Learning |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2407.14332 |