Summary of A Universal Metric Of Dataset Similarity For Cross-silo Federated Learning, by Ahmed Elhussein and Gamze Gursoy
A Universal Metric of Dataset Similarity for Cross-silo Federated Learning
by Ahmed Elhussein, Gamze Gursoy
First submitted to arxiv on: 29 Apr 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 This paper proposes a novel metric to assess dataset similarity in Federated Learning (FL) scenarios, addressing challenges of degradation in model performance due to non-identically distributed datasets across different sites. The metric is dataset-agnostic, privacy-preserving, and computationally efficient, requiring no model training. It establishes a theoretical connection with training dynamics in FL and demonstrates robustness on various datasets including synthetic, benchmark, and medical imaging datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier for healthcare providers to work together without sharing their patient data. They created a way to measure how similar two sets of data are, without actually looking at the data itself. This is important because when we try to train AI models together, our data might not be exactly the same, and that can affect how well the model works. The new metric shows that it’s possible to calculate this similarity in a way that respects people’s privacy. |
Keywords
» Artificial intelligence » Federated learning
