Summary of Can We Theoretically Quantify the Impacts Of Local Updates on the Generalization Performance Of Federated Learning?, by Peizhong Ju et al.
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?
by Peizhong Ju, Haibo Yang, Jia Liu, Yingbin Liang, Ness Shroff
First submitted to arxiv on: 5 Sep 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 proposed research investigates the generalization performance of Federated Learning (FL) with local updates. The study aims to quantify the impact of data heterogeneity and local updates on the learning process as it evolves. To achieve this, a comprehensive theoretical analysis is conducted using a linear model for both stationary and non-stationary cases. The findings provide closed-form expressions for the model error and demonstrate how the generalization performance changes with the number of rounds. Additionally, the research sheds light on the role of different configurations (model parameters, training samples) in determining overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way to train AI models without sharing sensitive data. Researchers are trying to understand how well this method works when the data is different and not all computers update their models at the same time. They used a simple math problem to test their ideas and found that the more often the models are updated, the better they get. This helps us understand what makes Federated Learning work or not work, which is important for using it in real-life situations. |
Keywords
» Artificial intelligence » Federated learning » Generalization
