Multi-tasking Federated Learning meets Blockchain to Foster Trust and Security in the Metaverse

Abstract

The Metaverse is a term that refers to a shared virtual space where users can interact with each other and with a virtual environment in real-time. The Metaverse has gained a lot of attention in recent years owing to the accelerated advancements in virtual and augmented realty technologies, which made it possible to create more immersive and highly interactive experiences for users. However, as users engage with the Metaverse, they generate a lot of data, which may include their location, activity, and interactions with other users. This data can be used to train multiple machine learning (ML) models to personalize content and ads, but it can also be exploited for more nefarious uses. The problem of user data collection and the sharing of sensitive and private data in the Metaverse is a challenge. Federated learning (FL) is a distributed machine learning (ML) mechanism that can avoid problems related to data sharing by having multiple devices work together to create a ML model while maintaining their local training data private. The utilization of FL in the Metaverse involves numerous contributors, thereby increasing the susceptibility to malicious activity, including attacks. To address this issue, a promising solution is the integration of blockchain technology, which can provide a trusted solution to the metaverse that allows for secure model learning. Due to frequent communication between FL and blockchain, FL needs to be adapted to the limited communication bandwidth and limited resources of the device. In this paper, we propose a blockchain-based FL framework to enhance security and trustworthiness in the Metaverse. The proposed solution utilizes a multi-task FL approach that leverages blockchain sharding to enhance the system’s throughput while simultaneously reducing resource requirements. By breaking the blockchain network into smaller shards, the processing power required for each shard is reduced, leading to an increase in overall throughput. The multi-task FL approach allows for the simultaneous training of multiple ML models, reducing the time and resources required to train each model individually. We formulate the model learning problem into a blockchain sharding problem and propose a bipartite matching solution for shard creation. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with a reliable behavior and informative dataset priority. Numerical results demonstrated that our framework has better performance than referenced solutions on the selected indicators.