no code implementations • 18 Mar 2023 • Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen
In this work, we propose a device-cloud collaborative controlled learning framework, called DC-CCL, enabling a cloud-side large vision model that cannot be directly deployed on the mobile device to still benefit from the device-side local samples.
no code implementations • 11 Nov 2022 • Yikai Yan, Chaoyue Niu, Fan Wu, Qinya Li, Shaojie Tang, Chengfei Lyu, Guihai Chen
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized.
no code implementations • 21 Oct 2022 • Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices.
no code implementations • 24 Jan 2022 • Renjie Gu, Chaoyue Niu, Yikai Yan, Fan Wu, Shaojie Tang, Rongfeng Jia, Chengfei Lyu, Guihai Chen
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to each individual user's local data distribution.
no code implementations • 18 Sep 2019 • Renjie Gu, Chaoyue Niu, Fan Wu, Guihai Chen, Chun Hu, Chengfei Lyu, Zhihua Wu
Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded.