Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication
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Abstract (arXiv)
In this paper we study the problem of signal detection in Gaussian noise in a distributed setting where the local machines in the star topology can communicate a single bit of information. We derive a lower bound on the Euclidian norm that the signal needs to have in order to be detectable. Moreover, we exhibit optimal distributed testing strategies that attain the lower bound.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2020.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 2.
4 papers.
- 2024Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraintscited by 7
- 2022Continual Mean Estimation Under User-Level Privacycited by 5
- 2024Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Testscited by 5
- 2022Optimal high-dimensional and nonparametric distributed testing under communication constraintscited by 3
- 2024Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints10 Jun 2024
- 2024Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests10 Jun 2024
- 2022Continual Mean Estimation Under User-Level Privacy20 Dec 2022
- 2022Optimal high-dimensional and nonparametric distributed testing under communication constraints2 Feb 2022