Summary of A Hassle-free Algorithm For Private Learning in Practice: Don’t Use Tree Aggregation, Use Blts, by H. Brendan Mcmahan et al.
A Hassle-free Algorithm for Private Learning in Practice: Don’t Use Tree Aggregation, Use BLTs
by H. Brendan McMahan, Zheng Xu, Yanxiang Zhang
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 The paper presents a novel approach to training on-device language models for mobile keyboard applications by combining federated learning (FL) with differential privacy (DP). The state-of-the-art method combines FL with DP via the DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm, which has two variants: tree aggregation and matrix factorization. However, tree aggregation suffers from suboptimal privacy/utility tradeoffs, while matrix mechanisms require expensive optimization and high runtime memory. To address these limitations, the paper introduces the Buffered Linear Toeplitz (BLT) mechanism for multi-participation scenarios, which maintains ease-of-use advantages of tree aggregation while matching matrix factorization in terms of utility and privacy. The authors evaluate BLT-DP-FTRL on the StackOverflow dataset and four on-device language model tasks in a production FL system, demonstrating its practicality and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it possible to train language models for mobile keyboards while keeping people’s data private. They combined two techniques: one that trains models on many devices at once (federated learning) and another that keeps the data safe (differential privacy). The current best approach uses a method called DP-FTRL, but it has some drawbacks. To fix these issues, they created a new way to do differential privacy called BLT-DP-FTRL. This new method is easy to use like the old tree aggregation method and performs just as well as the more complicated matrix factorization method. They tested this new method on a dataset of questions from StackOverflow and four real-world language model tasks, showing that it works well. |
Keywords
» Artificial intelligence » Federated learning » Language model » Optimization
