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

TL;DR

BF-Meta introduces a blockchain-based federated learning framework with decentralized aggregation and incentives, enhancing security, privacy, and user participation in metaverse services.

Contribution

It presents a novel secure, blockchain-enabled federated learning framework with decentralized aggregation and incentive mechanisms for metaverse applications.

Findings

Effective in mitigating malicious user influence

Enhances privacy and security in federated learning

Improves user participation through incentives

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.