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Found 4 papers, 1 papers with code
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation
Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/K^{1/2})$, where $K$ is the number of users.
When Classical Chinese Meets Machine Learning: Explaining the Relative Performances of Word and Sentence Segmentation Tasks
We consider three major text sources about the Tang Dynasty of China in our experiments that aim to segment text written in classical Chinese.
Communication Efficient Federated Learning over Multiple Access Channels
In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS.
Random Sampling for Distributed Coded Matrix Multiplication
Such approximate schemes make use of randomization techniques to speed up the computation process.
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