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

arXiv: https://arxiv.org/abs/2410.21675 · HTML: https://mlsanity.com/paper/2410.21675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI](https://mlsanity.com/paper/2206.07384/) (2022)
- [Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices](https://mlsanity.com/paper/1906.10893/) (2019)
- [Auto-weighted Robust Federated Learning with Corrupted Data Sources](https://mlsanity.com/paper/2101.05880/) (2021)
- [Exploring Blockchain Technology through a Modular Lens: A Survey](https://mlsanity.com/paper/2304.08283/) (2023)

## Abstract (arXiv)

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.