Summary of Smart Sampling: Helping From Friendly Neighbors For Decentralized Federated Learning, by Lin Wang et al.
Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning
by Lin Wang, Yang Chen, Yongxin Guo, Xiaoying Tang
First submitted to arxiv on: 5 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 The abstract discusses Federated Learning (FL), a privacy-preserving technique for sharing knowledge among clients while reducing communication costs. Decentralized FL (DFL) eliminates the need for a central server, allowing direct client-to-client communication and significant resource savings. However, data heterogeneity hinders performance improvement due to non-contributing neighbors. To address this, AFIND+, an efficient algorithm, is introduced for sampling and aggregating neighboring nodes in DFL. AFIND+ identifies helpful neighbors, adjusts the number of selected neighbors, and strategically aggregates models based on their contributions. Numerical results demonstrate that AFIND+ outperforms other sampling algorithms in DFL and is compatible with existing optimization algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning (FL) is a way for computers to share knowledge without sharing private information. It’s like having friends help each other learn new things, while keeping their own secrets safe. Decentralized FL (DFL) lets these computers talk directly to each other, which saves time and energy. But sometimes, not all the computers can help each other improve their learning. To solve this problem, researchers created a new way called AFIND+. It helps computers pick the right friends to work with, and then combines what they’ve learned in a smart way. This makes FL better at sharing knowledge while keeping things private. |
Keywords
* Artificial intelligence * Federated learning * Optimization
