28 citations · 55 across the 6 of their papers we have counts for
6 papers
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Chengfei Lv, Chaoyue Niu, Renjie Gu +17
To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system,…
Data-Free Evaluation of User Contributions in Federated Learning
Hongtao Lv, Zhenzhe Zheng, Tie Luo +5
Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device's private data and computing resources. A critical issues is to…
Toward Understanding the Influence of Individual Clients in Federated Learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng +4
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guaran…
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices
Yiwu Yao, Yuchao Li, Chengyu Wang +8
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices. In this paper, we present a novel quantized Winograd op…
MNN: A Universal and Efficient Inference Engine
Xiaotang Jiang, Huan Wang, Yiliu Chen +9
Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges o…
Secure Federated Submodel Learning
Chaoyue Niu, Fan Wu, Shaojie Tang +5
Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server.…