Mobile Devices Strategies in Blockchain-Based Federated Learning: A Dynamic Game Perspective

Fan, Sizheng; Wang, Zehua; Cai, Wei

doi:10.1109/tnse.2022.3163791

Cited by 12 publications

(2 citation statements)

References 34 publications

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“…Additionally, transmission scheduling in a wireless distributed learning network was explored to enhance the learning process in the distributed ecosystem. Fan et al [33] proposed a BCbased incentive mechanism for a trading platform and secured transfer via a game-theoretic approach that eventually presented an optimal Nash equilibrium. Regarding FL-based incentive and resource optimization, Jiang et al [34] proposed a scheme based on the Stackelberg equilibrium for resource optimization and incentive preservation.…”

Section: Related Workmentioning

confidence: 99%

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Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

Bhattacharya

Verma

Prasad

et al. 2023

Sensors

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The aim of the peer-to-peer (P2P) decentralized gaming industry has shifted towards realistic gaming environment (GE) support for game players (GPs). Recent innovations in the metaverse have motivated the gaming industry to look beyond augmented reality and virtual reality engines, which improve the reality of virtual game worlds. In gaming metaverses (GMs), GPs can play, socialize, and trade virtual objects in the GE. On game servers (GSs), the collected GM data are analyzed by artificial intelligence models to personalize the GE according to the GP. However, communication with GSs suffers from high-end latency, bandwidth concerns, and issues regarding the security and privacy of GP data, which pose a severe threat to the emerging GM landscape. Thus, we proposed a scheme, Game-o-Meta, that integrates federated learning in the GE, with GP data being trained on local devices only. We envisioned the GE over a sixth-generation tactile internet service to address the bandwidth and latency issues and assure real-time haptic control. In the GM, the GP’s game tasks are collected and trained on the GS, and then a pre-trained model is downloaded by the GP, which is trained using local data. The proposed scheme was compared against traditional schemes based on parameters such as GP task offloading, GP avatar rendering latency, and GS availability. The results indicated the viability of the proposed scheme.

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Section: Related Workmentioning

confidence: 99%

“…The wireless network radio resources are restricted, and posterior distribution is difficult Fan et al [33] 2021…”

Section: The Technology Of Autonomous Twins For Metaversesmentioning

confidence: 99%

Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

Bhattacharya

Verma

Prasad

et al. 2023

Sensors

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FedSAR for Heterogeneous Federated learning：A Client Selection Algorithm Based on SARSA

Chen,

Jing,

et al. 2024

Lecture Notes in Computer Science

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Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

Liu,

Chen,

et al. 2024

Knowl Inf Syst

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Mobile Devices Strategies in Blockchain-Based Federated Learning: A Dynamic Game Perspective

Cited by 12 publications

References 34 publications

Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

FedSAR for Heterogeneous Federated learning：A Client Selection Algorithm Based on SARSA

Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

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