Unravelling in Collaborative Learning
Authors: Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael Jordan, El-Mahdi El-Mhamdi, Alain Durmus
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a rigorous framework for analyzing collaborative learning with strategic agents having data distributions of varying quality. ... We show that when data quality is private, a naive aggregation strategy... results in a complete unravelling. ... We present solutions to unravelling. ... Theorem 1. Assume H1, H2, H3. Problem (8) admits a unique solution... Theorem 2 (Unravelling). Assume H1, H2, H3, and H4. Let E S J be the set of pure-strategy Nash equilibria... Theorem 3. Assume H1, H2, H3, H4 and H5. B = (1, . . . , 1)T is a Nash equilibrium under ˆΓ with probability 1 δ. |
| Researcher Affiliation | Academia | Aymeric Capitaine1 Etienne Boursier2 Antoine Scheid1 Eric Moulines1 Michael I. Jordan3 El-Mahdi El-Mhamdi1 Alain Durmus1 1 Centre de Mathématiques Appliquées CNRS École polytechnique Palaiseau, 91120, France 2 INRIA Saclay, Université Paris Saclay, LMO Orsay, 91400, France 3 Inria, Ecole Normale Supérieure, PSL Research University Paris, 75, France |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Methods are described in prose and mathematical notation. |
| Open Source Code | No | The paper does not contain any explicit statement or link providing concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical framework and does not conduct experiments on specific public or open datasets. The concept of 'samples' refers to theoretical data generation within the model. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not describe any experiments or their computational execution, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper does not describe any experiments or their computational execution, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not contain details about an experimental setup, hyperparameters, or system-level training settings. |