Federated Unlearning: a Perspective of Stability and Fairness
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917681493442560 |
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| author | Shao, Jiaqi Lin, Tao Cao, Xuanyu Luo, Bing |
| author_facet | Shao, Jiaqi Lin, Tao Cao, Xuanyu Luo, Bing |
| contents | This paper explores the multifaceted consequences of federated unlearning (FU) with data heterogeneity. We introduce key metrics for FU assessment, concentrating on verification, global stability, and local fairness, and investigate the inherent trade-offs. Furthermore, we formulate the unlearning process with data heterogeneity through an optimization framework. Our key contribution lies in a comprehensive theoretical analysis of the trade-offs in FU and provides insights into data heterogeneity's impacts on FU. Leveraging these insights, we propose FU mechanisms to manage the trade-offs, guiding further development for FU mechanisms. We empirically validate that our FU mechanisms effectively balance trade-offs, confirming insights derived from our theoretical analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Unlearning: a Perspective of Stability and Fairness Shao, Jiaqi Lin, Tao Cao, Xuanyu Luo, Bing Artificial Intelligence This paper explores the multifaceted consequences of federated unlearning (FU) with data heterogeneity. We introduce key metrics for FU assessment, concentrating on verification, global stability, and local fairness, and investigate the inherent trade-offs. Furthermore, we formulate the unlearning process with data heterogeneity through an optimization framework. Our key contribution lies in a comprehensive theoretical analysis of the trade-offs in FU and provides insights into data heterogeneity's impacts on FU. Leveraging these insights, we propose FU mechanisms to manage the trade-offs, guiding further development for FU mechanisms. We empirically validate that our FU mechanisms effectively balance trade-offs, confirming insights derived from our theoretical analysis. |
| title | Federated Unlearning: a Perspective of Stability and Fairness |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2402.01276 |