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Active learning has been proposed to reduce data annotation efforts by onlymanually labelling representative data samples for training. Meanwhile, recentactive learning applications have benefited a lot from cloud computing serviceswith not only sufficient computational resources but also crowdsourcingframeworks that include many humans in the active learning loop. However,previous active learning methods that always require passing large-scaleunlabelled 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 andimprove model stability. Specifically, we first introduce a human-in-the-loopteacher-student architecture to isolate unlabelled data from the task learner(teacher) on the cloud-side by maintaining an active learner (student) on theclient-side. During training, the task learner instructs the light-weightactive learner which then provides feedback on the active sampling criterion.To further enhance the active learner via large-scale unlabelled data, weintroduce multiple peer students into the active learner which is trained by anovel learning paradigm, including the In-Class Peer Study on labelled data andthe Out-of-Class Peer Study on unlabelled data. Lastly, we devise adiscrepancy-based active sampling criterion, Peer Study Feedback, that exploitsthe variability of peer students to select the most informative data to improvemodel stability. Extensive experiments demonstrate the superiority of theproposed PSL over a wide range of active learning methods in both standard andsensitive protection settings.

Responsible Active Learning via Human-in-the-loop Peer Study