Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
This work addresses security and trust issues in the Metaverse, though it appears incremental by combining existing quantum and federated learning concepts with blockchain.
The paper tackles the need for secure and transparent systems in the Metaverse by developing a decentralized quantum federated learning (QFL) framework, leveraging blockchain to enhance robustness against cyberattacks and demonstrating its practicality through extensive experiments.
With the emerging developments of the Metaverse, a virtual world where people can interact, socialize, play, and conduct their business, it has become critical to ensure that the underlying systems are transparent, secure, and trustworthy. To this end, we develop a decentralized and trustworthy quantum federated learning (QFL) framework. The proposed QFL leverages the power of blockchain to create a secure and transparent system that is robust against cyberattacks and fraud. In addition, the decentralized QFL system addresses the risks associated with a centralized server-based approach. With extensive experiments and analysis, we evaluate classical federated learning (CFL) and QFL in a distributed setting and demonstrate the practicality and benefits of the proposed design. Our theoretical analysis and discussions develop a genuinely decentralized financial system essential for the Metaverse. Furthermore, we present the application of blockchain-based QFL in a hybrid metaverse powered by a metaverse observer and world model. Our implementation details and code are publicly available 1.