Secure and Efficient Coded Multi-Access Edge Computing With Generalized Graph Neural Networks

doi:10.1109/TMC.2022.3172117

Secure and Efficient Coded Multi-Access Edge Computing With Generalized Graph Neural Networks

2023-09-01

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DOI：10.1109/TMC.2022.3172117

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We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs' types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) - a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.

Keywords:

Coded distributed computing

computational offloading

deep learning

graph neural networks

Internet of Things

Lagrange inter-polation

machine learning

multi-access edge computing

resource allocation

security

Secure and Efficient Coded Multi-Access Edge Computing With Generalized Graph Neural Networks

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