Summary of Synthetic Data Aided Federated Learning Using Foundation Models, by Fatima Abacha et al.
Synthetic Data Aided Federated Learning Using Foundation Models
by Fatima Abacha, Sin G. Teo, Lucas C. Cordeiro, Mustafa A. Mustafa
First submitted to arxiv on: 6 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 This paper proposes a novel approach to federated learning (FL) called Differentially Private Synthetic Data Aided Federated Learning Using Foundation Models (DPSDA-FL). The issue addressed is data heterogeneity in heterogeneous scenarios where the data distribution amongst FL participants is non-independent and identically distributed (Non-IID). DPSDA-FL improves local model training by leveraging differentially private synthetic data generated from foundation models. Experimental results on CIFAR-10 demonstrate a significant improvement in class recall and classification accuracy, with up to 26% and 9% increases, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with how computers learn together, called federated learning. When devices have different types of data, it can make the learning process worse. To fix this, the researchers created a new way to help local models on these devices by using fake but private data from bigger models. This makes the learning better and more accurate. The test results show that their method works really well, improving the accuracy of predictions. |
Keywords
* Artificial intelligence * Classification * Federated learning * Recall * Synthetic data
