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A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework

Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani

TL;DR

This work tackles fast, personalized model updates in the metaverse by marrying federated meta-learning with a blockchain-driven reputation system and a dual-game framework. The cooperative coalition formation game clusters heterogeneous MMLs by data/task similarity and past contributions, while a Stackelberg incentive mechanism coordinates resources and rewards between the MSP and participants, ensuring reliability and efficiency. The proposed GFML algorithm demonstrates improved training performance, reduced completion times, and increased metaverse utility, with robustness to misbehavior thanks to tamper-proof reputations. This approach enables scalable, trustworthy, and energy-conscious personalized learning for immersive metaverse services, with potential extensions to privacy-preserving embeddings and incentive optimization.

Abstract

The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users' tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users' historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users' energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes - improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.

A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework

TL;DR

This work tackles fast, personalized model updates in the metaverse by marrying federated meta-learning with a blockchain-driven reputation system and a dual-game framework. The cooperative coalition formation game clusters heterogeneous MMLs by data/task similarity and past contributions, while a Stackelberg incentive mechanism coordinates resources and rewards between the MSP and participants, ensuring reliability and efficiency. The proposed GFML algorithm demonstrates improved training performance, reduced completion times, and increased metaverse utility, with robustness to misbehavior thanks to tamper-proof reputations. This approach enables scalable, trustworthy, and energy-conscious personalized learning for immersive metaverse services, with potential extensions to privacy-preserving embeddings and incentive optimization.

Abstract

The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users' tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users' historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users' energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes - improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.
Paper Structure (22 sections, 31 equations, 9 figures, 3 tables, 2 algorithms)

This paper contains 22 sections, 31 equations, 9 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related works
  3. Framework and System model
  4. Framework
  5. System model
  6. Reliable heads selection and coalition formation
  7. Profit function of metaverse meta-learners
  8. Reputation model based on contribution
  9. Contribution quantification of metaverse meta-learners
  10. Global reputation score
  11. Task-specific reputation score
  12. Cooperative coalition game formulation
  13. Cooperative coalition formation algorithm
  14. Stackelberg game-based incentive mechanism for metaverse
  15. Game Equilibrium Analysis
  16. ...and 7 more sections

Figures (9)

  • Figure 1: Metaverse dual game framework.
  • Figure 2: System model of the FML for metaverse over a wireless network with multiple MML devices.
  • Figure 3: Performance of our proposed framework compared to existing baselines.
  • Figure 4: MSP and users utilities in our framework compared to existing works.
  • Figure 5: Test accuracy of passive MMLS.
  • ...and 4 more figures

Theorems & Definitions (3)

  • proof
  • proof
  • proof