[論文レビュー] Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness

Jiawen Kang, Jinbo Wen+8 more|arXiv (Cornell University)|Jul 29, 2023

Age of Information Optimization被引用数 8

ひとこと要約

論文は医療メタバースの privacy-preserving なクロスチェーン連合学習フレームワークを設計し、Prospect Theory に基づく AoI ベースの契約を導入してユーザーの新しいデータ共有を促進する

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 user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a cross-chain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical cross-chain 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 user-centric 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.

研究の動機と目的

• Motivate secure, privacy-preserving data sharing in healthcare metaverses using federated learning and blockchain.
• Enhance data freshness via AoI as a metric in time-sensitive FL tasks.
• Design a user-centric incentive mechanism under information asymmetry to encourage data sharing.
• Incorporate Prospect Theory to capture service provider risk attitudes in contract design.

提案手法

• Propose a cross-chain empowered federated learning framework with a hierarchical main chain and subchains for physical and virtual spaces.
• Use a cross-chain relay to securely publish tasks, train locally, and aggregate global models in a decentralized manner.
• Model data freshness with Age of Information (AoI) and derive a trade-off with service latency in FL.
• Formulate an AoI-based contract theory model under Prospect Theory to incentivize fresh data sharing.
• Reduce contract constraints from N2 IR/IC to N+1 constraints and derive EUT- and PT-based solutions for optimal contracts.

実験結果

リサーチクエスチョン

• RQ1 How can a cross-chain empowered FL framework improve privacy and security for healthcare metaverses?
• RQ2 How does AoI influence data freshness and service latency in time-sensitive FL tasks?
• RQ3 Can a PT-based AoI contract design effectively incentivize heterogeneous users to share fresh sensing data under information asymmetry?

主な発見

• A cross-chain empowered FL framework enables decentralized, privacy-preserving model training across physical and virtual spaces.
• AoI is used to quantify data freshness and its trade-off with FL service latency is analyzed.
• An AoI-based contract under Prospect Theory is proposed to incentivize data sharing from users with asymmetric information.
• The contract design is reformulated to reduce IR/IC constraints to a tractable set and provides mechanisms to compute optimal rewards for different update frequencies.
• Numerical results validate the effectiveness of the proposed privacy-preserving framework and incentive mechanism for healthcare metaverses.