Summary of Personalized Federated Learning Via Feature Distribution Adaptation, by Connor J. Mclaughlin et al.
Personalized Federated Learning via Feature Distribution Adaptation
by Connor J. Mclaughlin, Lili Su
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach to personalized federated learning (PFL), an extension of traditional federated learning (FL) frameworks. The authors address the challenges of heterogeneous client datasets by decomposing model training into shared representation learning and personalized classifier training. They frame representation learning as a generative modeling task, where representations are trained with a classifier based on the global feature distribution. The proposed algorithm, pFedFDA, efficiently generates personalized models by adapting global generative classifiers to local feature distributions. Through extensive computer vision benchmarks, the authors demonstrate significant improvements over current state-of-the-art in data-scarce settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning work better when different devices have different data. Imagine you’re trying to train a model that can recognize objects in pictures, but each device has its own unique set of pictures. This makes it hard for the model to learn and work well on all devices. The authors propose a new way to solve this problem by training individual models for each device, while still using information from other devices. They show that their method can improve results significantly when there is limited data available. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Representation learning
