Accelerating Vertical Federated Learning
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866916098705719296 |
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| author | Cai, Dongqi Fan, Tao Kang, Yan Fan, Lixin Xu, Mengwei Wang, Shangguang Yang, Qiang |
| author_facet | Cai, Dongqi Fan, Tao Kang, Yan Fan, Lixin Xu, Mengwei Wang, Shangguang Yang, Qiang |
| contents | Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Federated learning is proposed to ensure that all parties can collaboratively complete the training task while the data is not out of the local. Vertical federated learning is a specialization of federated learning for distributed features. To preserve privacy, homomorphic encryption is applied to enable encrypted operations without decryption. Nevertheless, together with a robust security guarantee, homomorphic encryption brings extra communication and computation overhead. In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically. We propose a straggler-resilient and computation-efficient accelerating system that reduces the communication overhead in heterogeneous scenarios by 65.26% at most and reduces the computation overhead caused by homomorphic encryption by 40.66% at most. Our system can improve the robustness and efficiency of the current vertical federated learning framework without loss of security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_11456 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Accelerating Vertical Federated Learning Cai, Dongqi Fan, Tao Kang, Yan Fan, Lixin Xu, Mengwei Wang, Shangguang Yang, Qiang Cryptography and Security Performance Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Federated learning is proposed to ensure that all parties can collaboratively complete the training task while the data is not out of the local. Vertical federated learning is a specialization of federated learning for distributed features. To preserve privacy, homomorphic encryption is applied to enable encrypted operations without decryption. Nevertheless, together with a robust security guarantee, homomorphic encryption brings extra communication and computation overhead. In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically. We propose a straggler-resilient and computation-efficient accelerating system that reduces the communication overhead in heterogeneous scenarios by 65.26% at most and reduces the computation overhead caused by homomorphic encryption by 40.66% at most. Our system can improve the robustness and efficiency of the current vertical federated learning framework without loss of security. |
| title | Accelerating Vertical Federated Learning |
| topic | Cryptography and Security Performance |
| url | https://arxiv.org/abs/2207.11456 |