BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
This addresses privacy and security issues for users in the metaverse, but it is incremental as it builds on existing federated learning and blockchain concepts.
The paper tackles security and privacy challenges in the metaverse by proposing BF-Meta, a blockchain-enhanced federated learning framework with decentralized model aggregation and an incentive mechanism, which experiments on five datasets show to be effective and applicable.
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.