Collaborative Active Learning in Conditional Trust Environment
Zan-Kai Chong Hiroyuki Ohsaki Bryan Ng
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 data sources and
insights without direct data exchange; and (c) it promotes cost-effectiveness
and resource efficiency through shared labeling costs. To realize these
benefits, we introduce a collaborative active learning framework designed to
fulfill the aforementioned objectives. We validate the effectiveness of the
proposed framework through simulations. The results demonstrate that
collaboration leads to higher AUC scores compared to independent efforts,
highlighting the framework's ability to overcome the limitations of individual
models. These findings support the use of collaborative approaches in active
learning, emphasizing their potential to enhance outcomes through collective
expertise and shared resources. Our work provides a foundation for further
research on collaborative active learning and its practical applications in
various domains where data privacy, cost efficiency, and model performance are
critical considerations.