Summary of On the Convergence Of Dp-sgd with Adaptive Clipping, by Egor Shulgin et al.
On the Convergence of DP-SGD with Adaptive Clipping
by Egor Shulgin, Peter Richtárik
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Optimization and Control (math.OC); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the application of Stochastic Gradient Descent (SGD) with gradient clipping in differentially private optimization. Specifically, they focus on quantile clipping, which has shown empirical success but lacks theoretical understanding. The authors provide a comprehensive convergence analysis of SGD with quantile clipping (QC-SGD), demonstrating that it suffers from bias problems similar to constant-threshold clipped SGD. They also show how these biases can be mitigated through careful selection of quantiles and step sizes. Furthermore, the paper establishes theoretical guarantees for differentially private optimization, providing practical guidelines for parameter selection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make machine learning models more private and secure. The authors look at a special technique called Stochastic Gradient Descent with gradient clipping that can help protect our personal data. They focus on a particular way of clipping called quantile clipping, which has been used before but not fully understood. The researchers do some math to figure out how this method works and provide rules for making it work better. This helps us make sure our models are not only smart but also safe. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Stochastic gradient descent
