Summary of Comparative Evaluation Of Clustered Federated Learning Methods, by Michael Ben Ali (irit) et al.
Comparative Evaluation of Clustered Federated Learning Methods
by Michael Ben Ali, Omar El-Rifai, Imen Megdiche, André Peninou, Olivier Teste
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A novel study explores the performance of two Clustered Federated Learning (CFL) algorithms in response to varying degrees of data heterogeneities, proposing a taxonomy for categorizing such scenarios. The research assesses the effectiveness of these methods on three image classification datasets, analyzing the resulting clusters using extrinsic clustering metrics. This contribution aims to provide insight into the relationship between CFL performance and data heterogeneity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to learn from many devices without sharing their data. When this method is used in real-world scenarios, it can be tricky because different devices have different kinds of information. To solve this problem, people use Clustered Federated Learning (CFL), which groups devices together based on the type of information they have. But until now, researchers haven’t been consistent about how to categorize these different groupings. This study looks at two top CFL methods and tests them using a new way to understand data heterogeneities. The goal is to figure out what makes each method work better or worse in different situations. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Image classification
