Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure
This addresses privacy and copyright concerns in AI collaborations, offering a novel approach for secure model training without data exchange, though it appears incremental in its application to zero-shot learning.
The paper tackles the problem of enabling collaboration between AI service providers and data owners without exposing sensitive data or models, by proposing a sentinel-guided zero-shot learning paradigm that uses a teacher model as a sentinel to guide a student model, achieving consistent outperformance in zero-shot and generalized zero-shot learning tasks, particularly under white-box protocols.
With increasing concerns over data privacy and model copyrights, especially in the context of collaborations between AI service providers and data owners, an innovative SG-ZSL paradigm is proposed in this work. SG-ZSL is designed to foster efficient collaboration without the need to exchange models or sensitive data. It consists of a teacher model, a student model and a generator that links both model entities. The teacher model serves as a sentinel on behalf of the data owner, replacing real data, to guide the student model at the AI service provider's end during training. Considering the disparity of knowledge space between the teacher and student, we introduce two variants of the teacher model: the omniscient and the quasi-omniscient teachers. Under these teachers' guidance, the student model seeks to match the teacher model's performance and explores domains that the teacher has not covered. To trade off between privacy and performance, we further introduce two distinct security-level training protocols: white-box and black-box, enhancing the paradigm's adaptability. Despite the inherent challenges of real data absence in the SG-ZSL paradigm, it consistently outperforms in ZSL and GZSL tasks, notably in the white-box protocol. Our comprehensive evaluation further attests to its robustness and efficiency across various setups, including stringent black-box training protocol.