Summary of Freqx: What Neural Networks Learn Is What Network Designers Say, by Zechen Liu
FreqX: What neural networks learn is what network designers say
by Zechen Liu
First submitted to arxiv on: 27 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 A novel interpretability method called FreqX is proposed to overcome challenges in Personalized Federal Learning (PFL), which enables clients to train personalized models without sharing private datasets. PFL faces issues of non-independent and identically distributed data, heterogeneous devices, lack of fairness, and unclear contributions, necessitating the interpretation of deep learning models. Existing methods fail to provide low-cost, privacy-preserving, and detailed explanations. FreqX leverages Signal Processing and Information Theory to generate attributions that include both concept and attribution information, outperforming baselines in terms of speed and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PFL allows clients to train personalized models without sharing private data. However, this process has some challenges like non-IID data, different devices, unfairness, and unclear contributions. To solve these problems, we need a way to understand how the model works. Current methods don’t provide clear explanations that are fast, private, and detailed. In this paper, we introduce FreqX, a new method for understanding deep learning models. Our results show that FreqX provides useful information about why the model made certain decisions. |
Keywords
» Artificial intelligence » Deep learning » Signal processing
