期刊
IEEE TRANSACTIONS ON INFORMATION THEORY
卷 68, 期 6, 页码 4070-4084出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2022.3150599
关键词
Testing; Estimation; Complexity theory; Signal to noise ratio; Distributed databases; Mutual information; Gaussian noise; Testing; distributed algorithms; hypothesis testing; minimax lower bounds; Gaussian noise; federated learning
资金
- European Research Council (ERC) through the European Union [101041064]
- European Research Council (ERC) [101041064] Funding Source: European Research Council (ERC)
This paper investigates the problem of signal detection in a distributed setting with Gaussian noise, where the local machines in the star topology can communicate a single bit of information. We derive a lower bound on the Euclidian norm required for detecting the signal and present optimal distributed testing strategies that achieve this lower bound.
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.
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