Resilient, Secure, and Private Coded Distributed Convolution Computing for Mobile-Assisted Metaverse

The Metaverse is recognized as the next-generation Internet that provides immersive interaction experiences for users. Convolutional neural networks (CNNs) play a crucial role in providing strong immersive experiences in the Metaverse. However, the Metaverse faces challenges in meeting the escalating demands for computing and storage resources due to the explosive growth of convolution tasks, resulting in severe performance degradation. To tackle these issues, coded distributed computing (CDC) is commonly employed. In this paper, we first propose an efficient and reliable mobile-assisted CDC framework to perform large-scale CNN training tasks for the Metaverse. In this framework, the various mobile devices act as workers contributing their resources to collaborate with each other to complete convolution operation tasks. Furthermore, we design a novel resilient, secure, and private coded convolution (RSPCC) scheme for the proposed framework. The RSPCC scheme achieves several significant performances. First, it substantially reduces computation latency compared to conventional convolution. Second, it efficiently mitigates an adverse impact of straggling workers returning results exceedingly slow. Third, we integrate a verifiable computing approach into the encoding/decoding process to check the correctness of the final computation results. Fourth, the PSPCC scheme considers the existence of colluding workers, providing information-theoretic privacy protection for input data. Finally, experimental results demonstrate that our proposed RSPCC scheme can significantly reduce execution time while ensuring the correctness of computation results within the CDC-based Metaverse framework.