Summary of Generalization Error Matters in Decentralized Learning Under Byzantine Attacks, by Haoxiang Ye and Qing Ling
Generalization Error Matters in Decentralized Learning Under Byzantine Attacks
by Haoxiang Ye, Qing Ling
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers investigate the generalization errors in decentralized learning algorithms that can withstand malicious agents (Byzantine). Decentralized learning enables model training across distributed agents without a central server, but malicious agents can impact performance. The study focuses on Byzantine-resilient DSGD algorithms and finds that generalization errors cannot be eliminated due to malicious agents, even with an infinite number of training samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are looking at how well decentralized learning works when some of the agents are trying to cheat or mislead. They’re studying a type of algorithm called Byzantine-resilient DSGD that can handle this kind of behavior. The results show that even with lots and lots of training data, there’s still a risk that the model won’t work well in new situations because of these malicious agents. |
Keywords
» Artificial intelligence » Generalization
