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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2209.15635 |
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| _version_ | 1866917218196914176 |
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| author | Li, Wenjie Xia, Shu-Tao Fan, Jiangke Zhang, Teng Wang, Xingxing |
| author_facet | Li, Wenjie Xia, Shu-Tao Fan, Jiangke Zhang, Teng Wang, Xingxing |
| contents | Traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-world industrial applications, where the learned model retains sufficient advantages of federated learning while supporting independent local serving. To achieve this goal, we propose the carefully designed Joint Privileged Learning framework (JPL) to i) alleviate the absence of the passive party's feature with federated equivalence imitation and ii) adapt to the heterogeneous full sample space with cross-branch rank alignment. Extensive experiments conducted on real-world advertising datasets validate the effectiveness of our method over baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_15635 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Vertical Semi-Federated Learning for Efficient Online Advertising Li, Wenjie Xia, Shu-Tao Fan, Jiangke Zhang, Teng Wang, Xingxing Machine Learning Information Retrieval Traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-world industrial applications, where the learned model retains sufficient advantages of federated learning while supporting independent local serving. To achieve this goal, we propose the carefully designed Joint Privileged Learning framework (JPL) to i) alleviate the absence of the passive party's feature with federated equivalence imitation and ii) adapt to the heterogeneous full sample space with cross-branch rank alignment. Extensive experiments conducted on real-world advertising datasets validate the effectiveness of our method over baseline methods. |
| title | Vertical Semi-Federated Learning for Efficient Online Advertising |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2209.15635 |