Incentive Framework for Cross-Device Federated Learning and Analytics With Multiple Tasks Based on a Multi-Leader-Follower Game

Yu, Yue; Chen, Dawei; Tang, Xiao; Song, Tiecheng; Hong, Choong Seon; Han, Zhu

doi:10.1109/tnse.2022.3190377

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…The above results indicate that the higher the bandwidth price set by RSU j, the less amount of bandwidth is purchased by UAV i. Given the aforementioned analysis, the UAVs can adapt their optimal bandwidth purchase strategies in response to the bandwidth selling price offered by RSUs, as shown in Algorithm 1 [37].…”

Section: Ifmentioning

confidence: 95%

“…Furthermore, the negative second-order derivative indicates the strict concavity of the utility function U i to the bandwidth demand strategy b j i employed by UAV i. Thus, the optimization problem for UAV i is a convex optimization problem [37].…”

Section: B Multi-leader Multi-follower Stackelberg Game Between Rsus ...mentioning

confidence: 99%

“…where λ i represents the Lagrangian multiplier [37]. The Karush-Kuhn-Tucker (KKT) conditions for problem Eq.…”

Section: B Multi-leader Multi-follower Stackelberg Game Between Rsus ...mentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation for Multi-Tenant Network Slicing: A Multi-Leader Multi-Follower Stackelberg Game Approach

Tran

2020

IEEE Trans. Veh. Technol.

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The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multifollower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes.

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Section: Ifmentioning

confidence: 95%

Section: B Multi-leader Multi-follower Stackelberg Game Between Rsus ...mentioning

confidence: 99%