Summary of Personalized Interpretation on Federated Learning: a Virtual Concepts Approach, by Peng Yan et al.
Personalized Interpretation on Federated Learning: A Virtual Concepts approach
by Peng Yan, Guodong Long, Jing Jiang, Michael Blumenstein
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Federated Learning (FL) method is proposed to tackle non-Independent and Identically Distributed (non-IID) data across clients, improving model performance and interpretability. Existing FL methods, such as robust FL and personalized FL, are extended to incorporate conceptual vectors representing interpretable concepts for end-users. These vectors can be pre-defined or learned via an optimization procedure or human-in-the-loop process. The proposed method enhances the robustness of training on non-IID data and is validated on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train AI models together with many devices, but it’s hard when the data isn’t similar across all devices. This paper proposes a new way to make federated learning work better by giving each device its own special features that help understand what kind of data they have. These features are like building blocks for humans to understand what the model is doing. The new method also makes training more robust and accurate, which means it can handle different types of data better. |
Keywords
* Artificial intelligence * Federated learning * Optimization
