Summary of R-conv: An Analytical Approach For Efficient Data Reconstruction Via Convolutional Gradients, by Tamer Ahmed Eltaras et al.
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients
by Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino, Stefano Di Carlo, Adnan Qayyum, Junaid Qadir
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 introduces an advanced data leakage method for federated learning that efficiently exploits convolutional layers’ gradients. The authors demonstrate a surprising finding: even with non-fully invertible activation functions like ReLU, they can analytically reconstruct training samples from gradients. This is the first analytical approach to successfully reconstruct convolutional layer inputs directly from gradients, bypassing the need to reconstruct output layers. The study highlights the significance of gradient constraints in convolutional layers and shows that existing analytical methods used to estimate the risk of gradient attacks lack accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps keep data private by sharing only gradients instead of raw data. However, some attacks can still leak private training data. Researchers found ways to reconstruct input data from fully connected layers, but these methods don’t work well for convolutional layers. This paper shows a new way to leak information from convolutional layers’ gradients. Even with special types of activation functions, they can figure out what the original data was. This is important because it means existing methods to stop attacks aren’t good enough. |
Keywords
» Artificial intelligence » Federated learning » Relu
