Summary of Gai-enabled Explainable Personalized Federated Semi-supervised Learning, by Yubo Peng et al.
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning
by Yubo Peng, Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 authors propose an explainable personalized federated learning (XPFL) framework that addresses challenges in applying federated learning to real-world scenarios. The framework consists of two components: generative AI (GAI) assisted personalized federated semi-supervised learning (GFed) and an explainable AI mechanism for FL (XFed). GFed uses a GAI model to learn from large unlabeled data and applies knowledge distillation-based semi-supervised learning to train local FL models. XFed utilizes decision trees and t-distributed stochastic neighbor embedding (t-SNE) to visualize the local models before and after aggregation. The authors validate the effectiveness of XPFL through simulation results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for mobile users to work together on artificial intelligence projects. Currently, there are some problems when people try to do this, like not having enough labels or data that isn’t the same everywhere. To solve these issues, the authors created two parts: a personalized learning system and a tool to explain how it works. The first part uses a special kind of AI to learn from lots of data without labels and then teaches other models. The second part helps understand what’s going on by showing pictures of the different models before and after they’re combined. The authors tested their idea and found that it works well. |
Keywords
» Artificial intelligence » Embedding » Federated learning » Knowledge distillation » Semi supervised
