Zobrazeno 1 - 10
of 11 328
pro vyhledávání: '"Xu, Zheng"'
Reducing the scanning time of very-low field (VLF) magnetic resonance imaging (MRI) scanners, commonly employed for stroke diagnosis, can enhance patient comfort and operational efficiency. The conventional parallel imaging (PI) technique for high-fi
Externí odkaz:
http://arxiv.org/abs/2411.07489
Autor:
Daly, Katharine, Eichner, Hubert, Kairouz, Peter, McMahan, H. Brendan, Ramage, Daniel, Xu, Zheng
Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling to millio
Externí odkaz:
http://arxiv.org/abs/2410.08892
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice. Existing analyse
Externí odkaz:
http://arxiv.org/abs/2410.01209
In this paper, we investigate potential randomization approaches that can complement current practices of input-based methods (such as licensing data and prompt filtering) and output-based methods (such as recitation checker, license checker, and mod
Externí odkaz:
http://arxiv.org/abs/2408.13278
The state-of-the-art for training on-device language models for mobile keyboard applications combines federated learning (FL) with differential privacy (DP) via the DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm. Two variants of DP-FTRL are use
Externí odkaz:
http://arxiv.org/abs/2408.08868
We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty
Externí odkaz:
http://arxiv.org/abs/2405.02341
The physical nature of pseudogap phase is one of the most important and intriguing problems towards understanding the key mechanism of high temperature superconductivity in cuprates. Theoretically, the square-lattice $t$-$J$ model is widely believed
Externí odkaz:
http://arxiv.org/abs/2404.16770
It has long been believed that doped quantum spin liquids (QSLs) can give rise to fascinating quantum phases, including the possibility of high-temperature superconductivity (SC) as proposed by P. W. Anderson's resonating valence bond (RVB) scenario.
Externí odkaz:
http://arxiv.org/abs/2404.05685
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-t
Externí odkaz:
http://arxiv.org/abs/2404.04360
Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data leaving the devices. SGD is the standard client optimizer for on device training in cross-device FL, fa
Externí odkaz:
http://arxiv.org/abs/2403.08100