Summary of Private and Federated Stochastic Convex Optimization: Efficient Strategies For Centralized Systems, by Roie Reshef and Kfir Y. Levy
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
by Roie Reshef, Kfir Y. Levy
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers tackle the challenge of preserving privacy in Federated Learning (FL) within centralized systems, considering both trusted and untrusted server scenarios. To address this issue, they analyze the setting within the Stochastic Convex Optimization (SCO) framework and develop methods that ensure Differential Privacy (DP) while maintaining optimal convergence rates for homogeneous and heterogeneous data distributions. The proposed approach, based on a recent stochastic optimization technique, offers linear computational complexity comparable to non-private FL methods and reduced gradient obfuscation. This work enhances the practicality of DP in FL, balancing privacy, efficiency, and robustness across various server trust environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping personal information safe when many people share data with a central system. Imagine you’re trying to learn how to recognize pictures of animals without sharing your own photos. The researchers came up with new ways to make this work while keeping the privacy of each person’s data. They tested these methods on different types of data and found that they were efficient and effective in keeping information private, even when not everyone trusted the central system. |
Keywords
» Artificial intelligence » Federated learning » Optimization
