Summary of Privacy Challenges in Meta-learning: An Investigation on Model-agnostic Meta-learning, by Mina Rafiei et al.
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning
by Mina Rafiei, Mohammadmahdi Maheri, Hamid R. Rabiee
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 The paper examines potential data leakage in a prominent meta-learning algorithm, Model-Agnostic Meta-Learning (MAML), which shares gradients between the meta-learner and task-learners. The authors analyze the gradient and its information about the task dataset, proposing membership inference attacks targeting the support and query sets. To safeguard privacy, they explore various noise injection methods to counter potential attacks. Experimental results demonstrate the effectiveness of these attacks on MAML and the efficacy of proper noise injection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meta-learning involves multiple learners sharing information to update their knowledge. In a prominent algorithm called Model-Agnostic Meta-Learning (MAML), gradients are shared between learners. This paper looks at how much information is shared in these gradients and uses that information to launch attacks on the task datasets. To make it harder for attackers, the authors try different ways of adding noise to the gradients. |
Keywords
» Artificial intelligence » Inference » Meta learning
