Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

Abstract

Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a usercentric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a crosschain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical crosschain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a usercentric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses.

Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

“…Related work on the Metaverse has been growing recently [28]- [31]. For instance, [28] and [29] are about the application of FL to the industrial Metaverse.…”

“…Related work on the Metaverse has been growing recently [28]- [31]. For instance, [28] and [29] are about the application of FL to the industrial Metaverse. [30] stands in the perspective of economics to assess and improve VR services for the Metaverse.…”

“…Simultaneously, it satisfies (23). Thus, a Newton-like method can be used to update (ν, β), which is done in (28).…”

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

“…However, the fusion of FL and Industrial Metaverse is not a simple combination of technologies. Industrial Metaverse [4] has extremely demanding requirements for edge intelligence, especially in terms of precision and efficiency [5]. This poses great challenges from both data and system perspectives under the current industrial network architecture.…”

“…As its four pillar technologies (Figure 1), Digital Twins [2], [24] collects real-time data from the real-world industrial environment and creates digital avatars of machines and managers in the virtual world. AI (especially FL and Blockchain) [5], [25]- [27] learn from these digital data and assist managers in making decisions or executing automated controls, where FL and Blockchain provide privacy and security guarantees respectively. In addition, actions performed in the virtual world will also be synchronized with the real world in real time.…”