  Active learning has been proposed to reduce data annotation efforts by only
manually labelling representative data samples for training. Meanwhile, recent
active learning applications have benefited a lot from cloud computing services
with not only sufficient computational resources but also crowdsourcing
frameworks that include many humans in the active learning loop. However,
previous active learning methods that always require passing large-scale
unlabelled data to cloud may potentially raise significant data privacy issues.
To mitigate such a risk, we propose a responsible active learning method,
namely Peer Study Learning (PSL), to simultaneously preserve data privacy and
improve model stability. Specifically, we first introduce a human-in-the-loop
teacher-student architecture to isolate unlabelled data from the task learner
(teacher) on the cloud-side by maintaining an active learner (student) on the
client-side. During training, the task learner instructs the light-weight
active learner which then provides feedback on the active sampling criterion.
To further enhance the active learner via large-scale unlabelled data, we
introduce multiple peer students into the active learner which is trained by a
novel learning paradigm, including the In-Class Peer Study on labelled data and
the Out-of-Class Peer Study on unlabelled data. Lastly, we devise a
discrepancy-based active sampling criterion, Peer Study Feedback, that exploits
the variability of peer students to select the most informative data to improve
model stability. Extensive experiments demonstrate the superiority of the
proposed PSL over a wide range of active learning methods in both standard and
sensitive protection settings.
