Federated One-Shot Learning with Data Privacy and Objective-Hiding
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913812888682496 |
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| author | Egger, Maximilian Urbanke, Rüdiger Bitar, Rawad |
| author_facet | Egger, Maximilian Urbanke, Rüdiger Bitar, Rawad |
| contents | Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has received much less attention.
We present a novel approach that addresses both concerns simultaneously, drawing inspiration from techniques in knowledge distillation and private information retrieval to provide strong information-theoretic privacy guarantees.
Traditional private function computation methods could be used here; however, they are typically limited to linear or polynomial functions. To overcome these constraints, our approach unfolds in three stages. In stage 0, clients perform the necessary computations locally. In stage 1, these results are shared among the clients, and in stage 2, the federator retrieves its desired objective without compromising the privacy of the clients' data. The crux of the method is a carefully designed protocol that combines secret-sharing-based multi-party computation and a graph-based private information retrieval scheme. We show that our method outperforms existing tools from the literature when properly adapted to this setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated One-Shot Learning with Data Privacy and Objective-Hiding Egger, Maximilian Urbanke, Rüdiger Bitar, Rawad Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Machine Learning Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has received much less attention. We present a novel approach that addresses both concerns simultaneously, drawing inspiration from techniques in knowledge distillation and private information retrieval to provide strong information-theoretic privacy guarantees. Traditional private function computation methods could be used here; however, they are typically limited to linear or polynomial functions. To overcome these constraints, our approach unfolds in three stages. In stage 0, clients perform the necessary computations locally. In stage 1, these results are shared among the clients, and in stage 2, the federator retrieves its desired objective without compromising the privacy of the clients' data. The crux of the method is a carefully designed protocol that combines secret-sharing-based multi-party computation and a graph-based private information retrieval scheme. We show that our method outperforms existing tools from the literature when properly adapted to this setting. |
| title | Federated One-Shot Learning with Data Privacy and Objective-Hiding |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2504.21182 |