MetaCrowd: Blockchain-Empowered Metaverse via Decentralized Machine Learning Crowdsourcing

Metaverse allows a 3D virtual mapping of the physical world to the digital world in which users interact with each other via digital avatars with a wide range of virtual activities. To realize this, the metaverse will inevitably employ numerous machine learning (ML) systems to enable the virtual-physical mapping process and offer intelligent virtual services to metaverse users (MUs). However, metaverse service providers (MSPs), who need ML models for their services (e.g., virtual events and healthcare services), may not have the expertise or resources required to build these underlying ML models. In addition, although ML models can be offered by a crowd of experienced ML workers (MLWs), the MLWs might not be able to collect the desired data for training their ML models due to privacy issues and the large-scale, distributed nature of the metaverse. In this paper, we propose MetaCrowd, a blockchain-based ML crowdsourcing framework that aims to overcome the mentioned issues and make ML accessible to a wide range of MUs and MSPs. Unlike traditional crowdsourcing systems which rely on central authorities, MetaCrowd is decentralized and automatic thanks to blockchain and smart contracts, thereby mitigating the single point of failure and trust issues. Experimental results illustrate the efficiency of MetaCrowd in both performance and cost. In addition, a decentralized application is also implemented and published widely to show its feasibility in practice.