Summary of Understanding Generalization Of Federated Learning: the Trade-off Between Model Stability and Optimization, by Dun Zeng et al.
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization
by Dun Zeng, Zheshun Wu, Shiyu Liu, Yu Pan, Xiaoying Tang, Zenglin Xu
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper explores Federated Learning (FL), a distributed machine learning approach that trains models across multiple devices while keeping data private. However, FL often faces challenges due to data heterogeneity, leading to inconsistent local optima among clients. These inconsistencies can cause unfavorable convergence behavior and generalization performance degradation. The authors propose an innovative framework, Libra, for algorithm-dependent excess risk minimization, which highlights the trade-offs between model stability and optimization. They show how the generalization of FL algorithms is affected by the interplay of algorithmic stability and optimization. Their findings suggest that larger local steps or momentum accelerate convergence but enlarge stability, while yielding a better minimum excess risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make machine learning models work well when different devices are working together to train them. Right now, this process can be tricky because the devices have different kinds of data, which makes it hard for the model to learn correctly. The authors came up with a new way to analyze how these models work and found that there’s a trade-off between making the model better at one device versus making sure it works well overall. |
Keywords
* Artificial intelligence * Federated learning * Generalization * Machine learning * Optimization
