Summary of Sdba: a Stealthy and Long-lasting Durable Backdoor Attack in Federated Learning, by Minyeong Choe et al.
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning
by Minyeong Choe, Cheolhee Park, Changho Seo, Hyunil Kim
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 paper introduces a novel backdoor attack mechanism called SDBA, designed specifically for natural language processing (NLP) tasks in federated learning (FL) environments. The authors analyze the vulnerability of LSTM and GPT-2 models to backdoor attacks, identifying the most susceptible layers for injection and developing techniques to ensure stealth and durability. Experimental results demonstrate that SDBA outperforms existing backdoors in terms of durability and can evade representative defense mechanisms, with notable performance on large language models like GPT-2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train machine learning models while keeping data private. But it has a problem – it’s vulnerable to hackers. The hackers can make the model do something bad by secretly adding some information. This paper makes a new kind of hacker attack that works well on language processing tasks. They tested this attack on two popular models and found that it’s very good at hiding and staying there for a long time. This means we need better ways to protect our models. |
Keywords
» Artificial intelligence » Federated learning » Gpt » Lstm » Machine learning » Natural language processing » Nlp
