Summary of Fedns: a Fast Sketching Newton-type Algorithm For Federated Learning, by Jian Li et al.
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
by Jian Li, Yong Liu, Wei Wang, Haoran Wu, Weiping Wang
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 proposed Federated Newton Sketch methods (FedNS) introduce a novel approach to tackle the issue of communicating Hessian matrices in federated learning while achieving fast convergence rates. By approximating the centralized Newton’s method and reducing the sketch size to match the effective dimension of the Hessian matrix, FedNS achieves super-linear convergence rates with respect to communication rounds for the first time. This is achieved through statistical learning-based convergence analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to make machine learning work better when many devices are involved. They wanted to find a way to make it faster and more efficient, so they created a new method called Federated Newton Sketch methods (FedNS). This method helps devices communicate with each other by sending only the most important information, instead of sending everything at once. The team tested their method and found that it works really well, even better than some existing methods. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
