Summary of Ba-bfl: Barycentric Aggregation For Bayesian Federated Learning, by Nour Jamoussi et al.
BA-BFL: Barycentric Aggregation for Bayesian Federated Learning
by Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou, Marios Kountouris
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Networking and Internet Architecture (cs.NI)
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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 tackles the problem of aggregation in Bayesian Federated Learning (BFL) by applying an information geometric perspective. The authors interpret the BFL aggregation step as finding the barycenter of trained posteriors for a specified divergence metric, recovering closed-form solutions for the reverse Kullback-Leibler and squared Wasserstein-2 barycenters. They analyze the performance of developed algorithms against state-of-the-art Bayesian aggregation methods in terms of accuracy, uncertainty quantification (UQ), model calibration (MC), and fairness. The authors also extend their analysis to Hybrid Bayesian Deep Learning (HBDL), studying how the number of Bayesian layers impacts performance metrics. Experimental results show that the proposed methodology matches state-of-the-art performance while providing a geometric interpretation of the aggregation phase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to combine information from different sources in a special kind of artificial intelligence called Federated Learning. The authors use a new way of thinking about combining this information, which is based on geometry and math. They show that their approach can perform just as well as the best existing methods while giving us a better understanding of what’s happening when we combine this information. |
Keywords
» Artificial intelligence » Deep learning » Federated learning
