Summary of Sprifed-omp: a Differentially Private Federated Learning Algorithm For Sparse Basis Recovery, by Ajinkya Kiran Mulay et al.
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
by Ajinkya Kiran Mulay, Xiaojun Lin
First submitted to arxiv on: 29 Feb 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 As machine learning educators, we must understand the challenges of sparse basis recovery when dealing with Federated Learning (FL) settings. In this paper, researchers develop a new differentially private algorithm called SPriFed-OMP to address this issue. The algorithm combines secure multi-party computation and differential privacy to efficiently recover the true sparse basis for linear models using only O(sqrt(p)) samples. This approach is an enhancement of the original SPriFed-OMP, which terminates in a small number of steps and outperforms previous state-of-the-art DP-FL solutions in terms of accuracy-privacy trade-offs. The authors also present an enhanced version called SPriFed-OMP-GRAD based on gradient privatization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn about the human brain, but you can’t get all the data from one place because it’s too big and complicated. That’s kind of like what happens in Federated Learning (FL) when we want to use lots of different computers to help us figure out a problem together. The trick is to keep each computer’s information private while still learning something new. This paper develops a way to do just that using an algorithm called SPriFed-OMP. It works by combining two important ideas: keeping data private and finding the most important parts of it. The authors show that their method can learn about really big problems with surprisingly few “brain cells” (or data points). They also came up with a way to make it even better, which they call SPriFed-OMP-GRAD. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
