用区块链保护元宇宙中的联邦学习，防作弊还激励用户参与
BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
- 用区块链实现去中心化模型聚合，避免单点故障
- 实验在5个数据集上验证，有效抵御恶意用户攻击
- 设计激励机制，根据用户行为反馈奖励，提升参与度
元宇宙作为社交与经济活动的革命性平台，提供各类虚拟服务的同时也带来安全与隐私挑战。可穿戴设备是连接现实世界与元宇宙的桥梁。为在不泄露用户隐私的前提下提供智能服务，利用联邦学习（FL）在本地可穿戴设备上训练模型是一种可行方案。然而，传统FL的集中式模型聚合易受外部攻击，存在单点故障风险。此外，缺乏激励机制可能导致用户参与度下降，进而影响模型性能与服务质量。本文提出BF-Meta，一种基于区块链的去中心化联邦学习框架，通过分布式模型聚合抵御恶意用户影响，保障元宇宙中虚拟服务的安全性。同时，设计激励机制，根据用户行为给予反馈奖励。在五个数据集上的实验验证了BF-Meta的有效性与适用性。
原文摘要 · Abstract (English)
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.
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