Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908738875555840 |
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| author | Lee, Kichang Jin, Jaeho Park, JaeYeon Kim, Songkuk Ko, JeongGil |
| author_facet | Lee, Kichang Jin, Jaeho Park, JaeYeon Kim, Songkuk Ko, JeongGil |
| contents | Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often address these issues separately, sacrificing either system robustness or model accuracy. This work introduces Tazza, a secure and efficient federated learning framework that simultaneously addresses both challenges. By leveraging the permutation equivariance and invariance properties of neural networks via weight shuffling and shuffled model validation, Tazza enhances resilience against diverse poisoning attacks, while ensuring 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07454 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning Lee, Kichang Jin, Jaeho Park, JaeYeon Kim, Songkuk Ko, JeongGil Machine Learning Artificial Intelligence 68T07 I.2.11 Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often address these issues separately, sacrificing either system robustness or model accuracy. This work introduces Tazza, a secure and efficient federated learning framework that simultaneously addresses both challenges. By leveraging the permutation equivariance and invariance properties of neural networks via weight shuffling and shuffled model validation, Tazza enhances resilience against diverse poisoning attacks, while ensuring 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. |
| title | Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning |
| topic | Machine Learning Artificial Intelligence 68T07 I.2.11 |
| url | https://arxiv.org/abs/2412.07454 |