Summary of Tazza: Shuffling Neural Network Parameters For Secure and Private Federated Learning, by Kichang Lee et al.
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning
by Kichang Lee, Jaeho Jin, JaeYeon Park, Songkuk Kim, JeongGil Ko
First submitted to arxiv on: 10 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 Federated learning’s decentralized approach enables model training without sharing raw data, ensuring data privacy. However, existing solutions often address security threats and accuracy separately, sacrificing system robustness or model precision. This work introduces Tazza, a secure and efficient federated learning framework that addresses both challenges simultaneously. By leveraging neural network properties via weight shuffling and shuffled model validation, Tazaa enhances resilience against diverse poisoning attacks while maintaining data confidentiality and high model accuracy. Comprehensive evaluations on various datasets and embedded platforms show that Tazza achieves robust defense with up to 6.7x improved computational efficiency compared to alternative schemes without compromising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning lets computers train models together without sharing sensitive data. But this approach has some big security problems. Some ways to fix these issues make the system stronger, but they also lower how well the model works. This research introduces a new way to do federated learning that solves both of these problems at once. It does this by changing how the neural networks work and making sure everything is secure and accurate. This new method was tested on many different datasets and worked really well. |
Keywords
» Artificial intelligence » Federated learning » Neural network » Precision
