Summary of Joint Optimization Of Resource Allocation and Data Selection For Fast and Cost-efficient Federated Edge Learning, by Yunjian Jia et al.
Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning
by Yunjian Jia, Zhen Huang, Jiping Yan, Yulu Zhang, Kun Luo, Wanli Wen
First submitted to arxiv on: 3 Jul 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 paper introduces Federated Edge Learning (FEEL), which trains machine learning models at the wireless edge with limited communication resources. To optimize FEEL’s performance, the authors propose jointly optimizing resource allocation and data selection. They rigorously model the training process, derive an upper bound on convergence rate, and transform the problem into two subproblems: resource allocation and data selection. The authors develop low-complexity algorithms for each subproblem using matching theory, convex-concave procedure, and gradient projection methods. Experimental results demonstrate the superiority of their proposed scheme. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train machine learning models on devices at the edge of a wireless network. The problem is that these devices have limited communication resources and sometimes receive mislabeled data. To solve this issue, the authors propose a new approach that jointly optimizes how to allocate resources and select which data to use for training. They develop an algorithm to do this efficiently and show through experiments that it performs better than other methods. |
Keywords
» Artificial intelligence » Machine learning
