Summary of Scrutinizing the Vulnerability Of Decentralized Learning to Membership Inference Attacks, by Ousmane Touat et al.
Scrutinizing the Vulnerability of Decentralized Learning to Membership Inference Attacks
by Ousmane Touat, Jezekael Brunon, Yacine Belal, Julien Nicolas, Mohamed Maouche, César Sabater, Sonia Ben Mokhtar
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Decentralized machine learning models can be trained collaboratively while keeping data private, but this requires sharing model parameters or gradients. However, such exchanges can be exploited to infer sensitive information about training data through privacy attacks like Membership Inference Attacks (MIA). To devise effective defense mechanisms, understanding the factors that increase or reduce vulnerability is crucial. This study explores the vulnerability of various decentralized learning architectures by varying graph structure, dynamics, and aggregation strategy across diverse datasets and data distributions. The key finding is that vulnerability to MIA is heavily correlated with local model mixing strategy and global mixing properties of the communication graph. Experimental results using four datasets illustrate these findings, while theoretical analysis provides insights into the mixing properties of various decentralized architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decentralized machine learning models are special because they let people train models together without sharing their data. But this can be a problem because it makes it easier for someone to figure out what kind of data was used to train the model. This is a problem called Membership Inference Attacks (MIA). To make these attacks harder, we need to understand what makes decentralized learning systems more or less vulnerable. In this study, we look at how different types of decentralized learning systems work and how they are affected by things like who they share information with and how they mix the information together. We found that how well a system is designed to reduce vulnerability to MIA depends on these factors. |
Keywords
» Artificial intelligence » Inference » Machine learning
