Summary of Distributionally Robust Clustered Federated Learning: a Case Study in Healthcare, by Xenia Konti et al.
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
by Xenia Konti, Hans Riess, Manos Giannopoulos, Yi Shen, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
First submitted to arxiv on: 9 Oct 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 Cross-silo Robust Clustered Federated Learning (CS-RCFL) algorithm tackles the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel approach that leverages the Wasserstein distance to construct ambiguity sets around each client’s empirical distribution. This enables evaluation of worst-case model performance and avoids biases caused by statistically heterogeneous client datasets through optimal clustering of clients into coalitions determined by a model-agnostic integer fractional program. The algorithm is analyzed for linear and logistic regression models, and a federated learning protocol is discussed that ensures the privacy of client distributions, a critical consideration in healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to combine different types of data from multiple sources into a single machine learning model. This is important because these sources might have different patterns or characteristics. The new approach uses something called the Wasserstein distance to make sure that the model is robust and can handle any changes in the data. It also finds the best way to group the data together so that the models are not biased by the differences between them. The algorithm is tested with real-world healthcare data, which is important because it shows how this approach could be used in a realistic scenario. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Logistic regression » Machine learning
