Summary of A Bayesian Framework For Clustered Federated Learning, by Peng Wu et al.
A Bayesian Framework for Clustered Federated Learning
by Peng Wu, Tales Imbiriba, Pau Closas
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML)
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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 paper presents a unified Bayesian framework for clustered federated learning (FL), addressing the challenge of handling non-independent and identically distributed (non-IID) client data. Clustered FL groups clients with similarly distributed data into clusters, enabling model personalization and knowledge sharing among peers. The proposed framework associates clients to clusters without requiring unique associations, increasing model performance in various experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps many devices learn together without sharing their data. Sometimes, the data on these devices is different or unbalanced, making it hard for them to work together. One way to solve this problem is by grouping similar devices together and having each group learn a model. This paper shows how to do this using a new framework that combines different ideas from machine learning and statistics. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
