13 citations · 26 across the 7 of their papers we have counts for
7 papers
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models
Yucheng Ding, Chaoyue Niu, Fan Wu +3
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage th…
On-Device Model Fine-Tuning with Label Correction in Recommender Systems
Yucheng Ding, Chaoyue Niu, Fan Wu +3
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 mob…
Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes
Shilan Jin, Rui Tuo, Akash Tiwari +5
This work is concerned with providing a principled decision process for stopping or tool-changing in a surface finishing process. The decision process is supposed to work for produ…
Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policy
Imtiaz Ahmed, Satish Bukkapatnam, Bhaskar Botcha +1
An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions by itself or even for…
Federated Submodel Optimization for Hot and Cold Data Features
Yucheng Ding, Chaoyue Niu, Fan Wu +4
We study practical data characteristics underlying federated learning, where non-i.i.d. data from clients have sparse features, and a certain client's local data normally involves…
Distributed Optimization over Block-Cyclic Data
Yucheng Ding, Chaoyue Niu, Yikai Yan +5
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains sever…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…