Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910687360450560 |
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| author | Tsoy, Nikita Mihalkova, Anna Todorova, Teodora Konstantinov, Nikola |
| author_facet | Tsoy, Nikita Mihalkova, Anna Todorova, Teodora Konstantinov, Nikola |
| contents | Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients' utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains Tsoy, Nikita Mihalkova, Anna Todorova, Teodora Konstantinov, Nikola Machine Learning Cryptography and Security Computer Science and Game Theory Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients' utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments. |
| title | Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains |
| topic | Machine Learning Cryptography and Security Computer Science and Game Theory |
| url | https://arxiv.org/abs/2403.06672 |