Summary of Banded Square Root Matrix Factorization For Differentially Private Model Training, by Nikita P. Kalinin et al.
Banded Square Root Matrix Factorization for Differentially Private Model Training
by Nikita P. Kalinin, Christoph Lampert
First submitted to arxiv on: 22 May 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 Differentially private model training relies heavily on matrix factorization techniques, but current state-of-the-art methods incur high computational costs due to complex optimization problems. The proposed BSR (Batched Square Root) approach revolutionizes this process by leveraging the standard matrix square root’s properties, allowing for efficient handling of large-scale problems. In the context of stochastic gradient descent with momentum and weight decay, analytical expressions for BSR reduce computational overhead to near zero. Theoretical bounds on approximation quality are established for both centralized and federated learning settings. Numerical experiments confirm that models trained using BSR achieve parity with existing methods while avoiding their computational burdens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to train a model, but you need to make sure it keeps your data private. Current methods do this by solving complex math problems, which takes a long time. Researchers have found a way to speed up this process using something called BSR (Batched Square Root). This new method is efficient and can handle big datasets. It works just as well as the old methods but much faster. The scientists tested it and found that it’s just as good at making predictions while keeping data private. |
Keywords
» Artificial intelligence » Federated learning » Optimization » Stochastic gradient descent
