Federated Learning Meets Blockchain to Secure the Metaverse

Abstract

The development of the Metaverse is completely changing how business is done in the physical world. The Metaverse considerably improves intelligent manufacturing by mapping out operations and spreading them into virtual space. The Metaverse can access data from numerous production and operation lines thanks to the Internet of Things (IoT), enabling efficient data analysis and decision-making. However, the problem of sharing sensitive and private data remains a challenge when integrating the Metaverse with IoT. Federated learning (FL) has emerged as a distributed machine learning (ML) setting that can overcome the security problems related to data sharding With FL, several devices can work together to create an ML model under the direction of a central server while maintaining the privacy and security of their local training data. FL in the Metaverse continues to face significant challenges due to a lack of transparency, learning forgetting caused by streaming industrial data, and problems with non-independent and identically dispersed (non-iid) data. In this paper, we develop a FL framework for transparent and secure model learning in the Metaverse using blockchain technology. The blockchain ledger stores and verifies the model updates which ensures that all updates are tamper-proof and transparent to all parties involved. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with reliable behavior priority. The numerical result demonstrates that our framework performed better on the chosen indicators.

Topics & Concepts