Work

Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling

Year: 2022

Type: article

Abstract: , which is a massive dataset collected from public and heterogeneous sources (e.g., Internet images). We develop a novel strategy called Efficient Collaborative Open-source Sampling (ECOS) to construct a proximal proxy dataset from open-source data for cloud training, in lieu of client data. ECOS probes open-source data on the cloud server to sense the distribution of client data via a communication- and computation-efficient sampling process, which only communicates a few compressed public features and client scalar responses. Extensive empirical studies show that the proposed ECOS improves the quality of automated client labeling, model compression, and label outsourcing when applied in various learning scenarios.

Source: PubMed

DOI: 10.48550/arxiv.2210.12575

Authors: Hong, Junyuan, Lyu, Lingjuan, Zhou, Jiayu, Spranger, Michael

Institutions: Michigan State University, Sony (Japan)

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institute on Aging, Office of Naval Research