Summary of Efficient Federated Low Rank Matrix Completion, by Ahmed Ali Abbasi and Namrata Vaswani
Efficient Federated Low Rank Matrix Completion
by Ahmed Ali Abbasi, Namrata Vaswani
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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 proposes Alternating Gradient Descent and Minimization (AltGDmin), a novel approach for efficiently solving low-rank matrix completion in federated settings. This problem involves recovering a large matrix from partial entries when the rank of the matrix is much smaller than its dimensions. Theoretical guarantees show that AltGDmin achieves the best communication efficiency, is one of the fastest, and has the second-best sample complexity among all iterative solutions to low-rank matrix completion. The paper also proves two important corollaries: a guarantee for solving noisy low-rank matrix completion problems and an improved sample complexity guarantee for AltMin, the fastest centralized solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low rank matrix completion is a problem that involves recovering a large matrix from partial entries when the rank of the matrix is much smaller than its dimensions. This paper proposes a new way to solve this problem in a distributed setting called Alternating Gradient Descent and Minimization (AltGDmin). It’s an efficient solution that uses a combination of gradient descent and minimization techniques. The authors also show that their method can be used to solve noisy versions of the problem, which is important for real-world applications. |
Keywords
» Artificial intelligence » Gradient descent
