# Unravelling in Collaborative Learning

arXiv: https://arxiv.org/abs/2407.14332 · HTML: https://mlsanity.com/paper/2407.14332/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2024.

## Built on by (most cited first, 6 of 6)

- [CoBo: Collaborative Learning via Bilevel Optimization](https://mlsanity.com/paper/2409.05539/) (2024)
- [Multiplayer Federated Learning: Reaching Equilibrium with Less Communication](https://mlsanity.com/paper/2501.08263/) (2025)
- [Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution](https://mlsanity.com/paper/2407.15881/) (2024)
- [Adaptive collaboration for online personalized distributed learning with heterogeneous clients](https://mlsanity.com/paper/2507.06844/) (2025)
- [Approaching the Harm of Gradient Attacks While Only Flipping Labels](https://mlsanity.com/paper/2503.00140/) (2025)
- [Microeconomic Foundations of Multi-Agent Learning](https://mlsanity.com/paper/2601.03451/) (2026)

## Abstract (arXiv)

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.