Summary of Distributed, Communication-efficient, and Differentially Private Estimation Of Kl Divergence, by Mary Scott et al.
Distributed, communication-efficient, and differentially private estimation of KL divergence
by Mary Scott, Sayan Biswas, Graham Cormode, Carsten Maple
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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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 proposes novel algorithms for estimating the KL divergence of data across federated models under differential privacy. The authors analyze their theoretical properties and present an empirical study of their performance, exploring parameter settings that optimize the accuracy of the algorithm for various applications. Their private estimators achieve comparable accuracy to a baseline algorithm without privacy guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us measure how much data changes when it’s shared between different groups or models. This is important for tasks like machine learning and analytics. The problem is that sharing this information can be bad for privacy or take too long. To fix this, the authors create new algorithms to estimate how much the data has changed without revealing too much. They test these algorithms and show they work well in different situations. |
Keywords
* Artificial intelligence * Machine learning
