Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929660996091904 |
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| author | Salgia, Sudeep Pavlovic, Nikola Chi, Yuejie Zhao, Qing |
| author_facet | Salgia, Sudeep Pavlovic, Nikola Chi, Yuejie Zhao, Qing |
| contents | We consider the problem of differentially private stochastic convex optimization (DP-SCO) in a distributed setting with $M$ clients, where each of them has a local dataset of $N$ i.i.d. data samples from an underlying data distribution. The objective is to design an algorithm to minimize a convex population loss using a collaborative effort across $M$ clients, while ensuring the privacy of the local datasets. In this work, we investigate the accuracy-communication-privacy trade-off for this problem. We establish matching converse and achievability results using a novel lower bound and a new algorithm for distributed DP-SCO based on Vaidya's plane cutting method. Thus, our results provide a complete characterization of the accuracy-communication-privacy trade-off for DP-SCO in the distributed setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization Salgia, Sudeep Pavlovic, Nikola Chi, Yuejie Zhao, Qing Machine Learning Information Theory We consider the problem of differentially private stochastic convex optimization (DP-SCO) in a distributed setting with $M$ clients, where each of them has a local dataset of $N$ i.i.d. data samples from an underlying data distribution. The objective is to design an algorithm to minimize a convex population loss using a collaborative effort across $M$ clients, while ensuring the privacy of the local datasets. In this work, we investigate the accuracy-communication-privacy trade-off for this problem. We establish matching converse and achievability results using a novel lower bound and a new algorithm for distributed DP-SCO based on Vaidya's plane cutting method. Thus, our results provide a complete characterization of the accuracy-communication-privacy trade-off for DP-SCO in the distributed setting. |
| title | Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2501.03222 |