6 papers
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs
H. Brendan McMahan, Zheng Xu, Yanxiang Zhang
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-R…
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
Yanxiang Zhang, Zheng Xu, Shanshan Wu +2
Error correction is an important capability when applying large language models (LLMs) to facilitate user typing on mobile devices. In this paper, we use LLMs to synthesize a high-…
Neural Search Space in Gboard Decoder
Yanxiang Zhang, Yuanbo Zhang, Haicheng Sun +4
Gboard Decoder produces suggestions by looking for paths that best match input touch points on the context aware search space, which is backed by the language Finite State Transduc…
MT-SNN: Enhance Spiking Neural Network with Multiple Thresholds
Xiaoting Wang, Yanxiang Zhang
Spiking neural networks (SNNs) present a promising energy efficient alternative to traditional Artificial Neural Networks (ANNs) due to their multiplication-free operations enabled…
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Shanshan Wu, Zheng Xu, Yanxiang Zhang +2
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…
Proofread: Fixes All Errors with One Tap
Renjie Liu, Yanxiang Zhang, Yun Zhu +6
The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users' typing experience. This paper demonstrates Proofread, a novel Gboard fea…