Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
This addresses privacy and bandwidth limitations for devices outsourcing training, though it is incremental as it builds on existing open-source data methods.
The paper tackles the problem of outsourcing deep learning model training to a cloud server without uploading sensitive client data by proposing ECOS, which uses open-source data to create a proxy dataset, resulting in improved client labeling, model compression, and label outsourcing in various scenarios.
As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training at a low-power and cost-effective end device. Traditional outsourcing requires uploading device data to the cloud server, which can be infeasible in many real-world applications due to the often sensitive nature of the collected data and the limited communication bandwidth. To tackle these challenges, we propose to leverage widely available open-source data, 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.