BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse

TL;DR

BF-Meta, a secure blockchain-empowered FL framework with decentralized model aggregation with an incentive mechanism to give feedback to users based on their behaviors is proposed to mitigate the negative influence of malicious users and provide secure virtual services in the metaverse.

Abstract

The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the metaverse. To provide intelligent services without revealing users' privacy in the metaverse, leveraging federated learning (FL) to train models on local wearable devices is a promising solution. However, centralized model aggregation in traditional FL may suffer from external attacks, resulting in a single point of failure. Furthermore, the absence of incentive mechanisms may weaken users' participation during FL training, leading to degraded performance of the trained model and reduced quality of intelligent services. In this paper, we propose BF-Meta, a secure blockchain-empowered FL framework with decentralized model aggregation, to mitigate the negative influence of malicious users and provide secure virtual services in the metaverse. In addition, we design an incentive mechanism to give feedback to users based on their behaviors. Experiments conducted on five datasets demonstrate the effectiveness and applicability of BF-Meta.

Figures (6)

• Figure 1: System overview of BF-Meta.
• Figure 2: The workflow of BF-Meta. The left and right parts indicate the workflow of the client and the server sides, respectively.
• Figure 3: Accuracy of BF-Meta on five datasets.
• Figure 4: Accuracy over epochs of FedAvg and BF-Meta.
• Figure 5: Comparison of accuracy of FedAvg and BF-Meta with distinct rates of malicious users.