Federated Learning (FL) enhances data privacy for End Equipment Workers (EWs) by enabling the sharing of model parameters instead of raw data. However, energy constraints and individual self-interest may discourage EWs from participating or slow down training, ultimately affecting the performance of the global FL model. To address these challenges, we propose a three-stage Stackelberg game-based framework that leverages wireless power to incentivize participation while ensuring the successful completion of FL tasks. In this framework, the Base Station (BS) publishes FL task and seeks to obtain an improved global model at a reduced cost. EWs train local models, aiming to maximize their payments while minimizing energy consumption. Meanwhile, the Charging Service Provider (CSP) supplies energy to EWs via Wireless Power Transfer (WPT) during model training and uploading, charging appropriate fees for the service. We employ the backward induction method to analyze the proposed game problem, proving the existence of a unique Stackelberg equilibrium and Nash equilibrium. Furthermore, we propose the Trust Region Method (TRM) to solve the unit payment strategy problem of BS. Extensive simulations validate that our method consistently outperforms benchmark schemes, achieving higher average utility across a wide range of scenarios.

Mobile Edge Computing (MEC) significantly enhances service response speeds and improves the Quality of Service (QoS) by facilitating the offloading of computation-intensive tasks from Mobile Users (MUs) to nearby Edge Servers (ESs). However, due to the inherent selfishness of involved entities, MUs may be unwilling to offload tasks without reasonable resource pricing, and ESs may lack motivation to provide computation resources without appropriate compensation. Furthermore, improving the utilization of ESs' computation resources and achieving efficient task scheduling remains a major challenge. To ad dress these issues, we introduce a profitable broker between ESs and MUs, and propose TORP, a Two-stage Stackelberg Game based Computation Offloading and Resource Pricing mechanism, to maximize the utility of each entity. Specifically, we model the interactions among three entities (i.e., the broker, MUs, and ESs) as a two-stage Stackelberg game, where the interactions between the broker and ESs is defined as Stage I, while the interactions between the broker and MUs is defined as Stage II. By using the backward induction method, we theoretically prove the Stackelberg Equilibrium (SE) for each stage of the two-stage Stackelberg, and the SE of the whole game. Then, recognizing that the optimization problem is a Mixed-Integer Nonlinear Programming (MINLP) problem, an Alternating Iteration-Based Resource Pricing and Task Offloading Algorithm (AIPOA) is proposed to obtain the optimal solution. Finally, we perform extensive simulations comparing TORP against multiple base lines. Experimental results show that TORP achieves substantial improvements, enhancing the utilities of three entities by about 2.00%-52.69% under different scenarios.

Vehicle edge computing can effectively ensure the quality of experience for user vehicles (UVs), but road side units (RSUs) with limited resources may not be able to handle intensive tasks under high traffic conditions. In this case, worker vehicles (WVs) with idle resources can share resources to alleviate the pressure on RSUs. However, selfish WVs may be reluctant to share idle computation resources without any rewards. In addition, the optimization problems in previous research are relatively simple and cannot be applied to complex scenarios. To address the above challenges, we propose an incentive-driven partial offloading framework aiming to maximize social welfare. In particular, the computing service provider (CSP) managing RSUs first determines resource prices and offloading rates with UVs, while also determining contract terms with WVs. Then, it generates the optimal task scheduling strategy and notifies the UVs to offload tasks to the corresponding WVs. Considering that maximizing social welfare is a mixed-integer nonlinear programming (MINLP) problem, we design the hybrid proximal policy optimization (HPPO)-based task offloading and resource allocation algorithm (HORA) with a hybrid action space to directly solve the original problem. Finally, extensive simulation results show that HORA outperforms other baseline methods across various scenarios, and the contract terms meet the constraints of individual rationality (IR) and incentive compatibility (IC).

By sharing model parameters instead of raw data to train machine models, Federated Learning (FL) can protect End equipment Workers (EWs)’ data privacy. However, due to energy constraints and selfishness, EWs may not be willing to participate or train slowly, which affects the performance of global FL model. To address these issues, we propose a three-stage Stackelberg game-based wireless powered FL framework to incentivize all players to participate in the system while ensuring the successful completion of FL tasks. Specifically, Base Station (BS) publishes the FL task and wants to obtain a better FL model at a lower cost. EWs train local FL models, and want to get more payment with less energy consumption. When EWs train and upload their local models, Charging Service Provider (CSP) transmits energy to them via Wireless Power Transfer (WPT) while charging fees. In order to obtain the optimal strategy for all participants, we analyze the proposed game problem using the backward induction method. Meanwhile, we prove that the unique Stackelberg equilibrium and Nash equilibrium can be obtained, and we obtain the approximate optimal solution of BS using the subgradient method. Finally, extensive simulations are conducted to evaluate the performance of the proposed method in different scenarios. The results show that the proposed method improves the utility of three parties by an average of 19.09% - 51.86% compared with the benchmark methods.