Hongjing Li
ABSTRACT
Considered the development paradigm of the next generation of the Internet after the network and mobile Internet revolution, the metaverse has received extensive attention. However, potential security problems in the metaverse hinder its further development. Among them, federated learning (FL), as a distributed learning framework that shares the important asset User Generated Content (UGC) in the meta-universe, is unable to withstand hidden poisoning attacks and is still threatened by security vulnerabilities. Therefore, this paper proposes a cohort-based credit evaluation method for federated learning (CoCE) to address the privacy and integrity deficiencies of traditional federal learning. In this article, by constructing cohorts, we alleviate the problem of uneven data distribution in federated learning and achieve multitask learning. In addition, we combine cohort mechanisms to evaluate the comprehensive credit of clients in terms of the number of customers, data distribution, and model preferences, and to screen out potential attackers in the course of federated learning. Finally, we have carried out experiments on open datasets to verify the robustness and superiority of CoCE.
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Index Terms
- Cohort-based Federated Learning Credit Evaluation Method in the Metaverse
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