Collaborative Active Learning in Conditional Trust Environment

arXiv:2403.18436v1 Announce Type: new
Abstract: In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. Instead, the collaborators share prediction results from the new domain and newly acquired labels. This collaboration offers several advantages: (a) it addresses privacy and security concerns by eliminating the need for direct model and data disclosure; (b) it enables the use of different …

abstract acquired active learning arxiv capabilities collaboration collaborative cs.lg data domain environment explore labels machine machine learning multiple paper paradigm prediction results trust type