A Secure and Robust Knowledge Transfer Framework via Stratified-Causality Distribution Adjustment in Intelligent Collaborative Services

The rapid development of device-edge-cloud collaborative computing techniques has actively contributed to the popularization and application of intelligent service models. The intensity of knowledge transfer plays a vital role in enhancing the performance of intelligent services. However, the existing knowledge transfer methods are mainly implemented through data fine-tuning and model distillation, which may cause the leakage of data privacy or model copyright in intelligent collaborative systems. To address this issue, we propose a secure and robust knowledge transfer framework through stratified-causality distribution adjustment (SCDA) for device-edge-cloud collaborative services. Specifically, a simple yet effective density-based estimation is first employed to obtain uncertainty scores that guide the space stratification, which is conducive to reconstructing low-density distribution regions from high-density distribution regions more adaptively and accurately. Subsequently, we devise a novel causality-aware generative model to generate synthetic features for the out-of-distribution domain by exploring the relationship between factors and variables. Ultimately, we introduce a cycle-consistent minimax optimization mechanism to ensure the effectiveness and dependability of knowledge transfer through the influence minimization and the diversity maximization. Furthermore, extensive experiments demonstrate that our scheme can protect the security of data privacy and model copyright in intelligent collaborative services through adaptive distribution adjustment.