Summary of How Does the Smoothness Approximation Method Facilitate Generalization For Federated Adversarial Learning?, by Wenjun Ding et al.
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?
by Wenjun Ding, Ying An, Lixing Chen, Shichao Kan, Fan Wu, Zhe Qu
First submitted to arxiv on: 11 Dec 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 Federated Adversarial Learning (FAL) frameworks, focusing on robustness against adversarial attacks in federated learning. Two popular FAL algorithms, Vanilla FAL (VFAL) and Slack FAL (SFAL), are evaluated using three smooth approximation methods: Surrogate Smoothness Approximation (SSA), Randomized Smoothness Approximation (RSA), and Over-Parameterized Smoothness Approximation (OPSA). The paper analyzes the generalization performance of these algorithms, identifying RSA as the most effective method in reducing generalization error. Furthermore, it recommends employing SFAL in highly data-heterogeneous scenarios to mitigate performance deterioration. This study aims to develop more efficient FAL algorithms by designing new metrics and dynamic aggregation rules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make sure that our computers can learn from lots of different devices without getting tricked by bad information. They look at two ways (VFAL and SFAL) that we can do this, and they try three different methods to make it work better. One method works really well! They also found out that if the devices have very different kinds of data, we should use a special way (SFAL) to make sure everything stays consistent. |
Keywords
» Artificial intelligence » Federated learning » Generalization
