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.

通过合作主动学习，利用多个协作者的机器学习能力，不泄露现有数据和模型，而是共享新领域的预测结果和新获取的标签，以解决隐私和安全问题、提高成本效益和资源利用效率，促进模型性能，并为进一步研究合作主动学习及其在各个领域的实际应用提供了基础。

条件信任环境下的协作主动学习

Collaborative Active Learning in Conditional Trust Environment

In collaborative active learning, where multiple agents try to learn labels
from a common hypothesis, we introduce an innovative framework for incentivized
collaboration. Here, rational agents aim to obtain labels for their data sets
while keeping label complexity at a minimum. We focus on designing (strict)
individually rational (IR) collaboration protocols, ensuring that agents cannot
reduce their expected label complexity by acting individually. We first show
that given any optimal active learning algorithm, the collaboration protocol
that runs the algorithm as is over the entire data is already IR. However,
computing the optimal algorithm is NP-hard. We therefore provide collaboration
protocols that achieve (strict) IR and are comparable with the best known
tractable approximation algorithm in terms of label complexity.

在协作式主动学习中，我们引入了一种创新的奖励机制协作框架，旨在最小化标签复杂性的同时使理性代理获得其数据集的标签。我们提供了实现严格个体理性 (IR) 并在标签复杂性方面与最佳已知可行近似算法相媲美的协作协议。