Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2023

Abstract

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.

Keywords

Adaptation models, Artificial intelligence, causal perception, Collaboration, cycle-consistent minimax optimization, Data models, Intelligent collaborative service, Knowledge transfer, knowledge transfer, Robustness, space stratification, Task analysis

Discipline

Information Security | Theory and Algorithms

Research Areas

Cybersecurity

Publication

IEEE Transactions on Computers

First Page

1

Last Page

14

ISSN

0018-9340

Identifier

10.1109/TC.2023.3318403

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TC.2023.3318403

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