Summary of A Historical Trajectory Assisted Optimization Method For Zeroth-order Federated Learning, by Chenlin Wu et al.
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning
by Chenlin Wu, Xiaoyu He, Zike Li, Jing Gong, Zibin Zheng
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 method improves upon traditional isotropic random direction-based gradient estimation by incorporating historical solution trajectories to encourage exploration and improve convergence in zeroth-order federated learning. By using a covariance matrix that combines thin projection matrices and historical trajectories, the method leverages the geometric features of the objective landscape, leading to reduced estimation errors and improved optimization performance. This approach is shown to align with existing zeroth-order federated optimization methods while introducing minimal overheads in communication and local computation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists came up with a new way to help machines learn from each other without sharing their data. They wanted to improve the process of estimating how well an algorithm will do on different problems. To do this, they used information about how good past solutions were at solving similar problems. This helped them find better ways to explore and solve problems in a distributed learning setting. The new method was tested against other popular methods and showed promising results. |
Keywords
» Artificial intelligence » Federated learning » Optimization
