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.

In this paper, a symbiotic radio (SR) system against a powerful eavesdropper (Willie) has been investigated. Specifically, the primary transmitter (Alice) enhances the reliability of communication by leveraging prior information of legitimate links. Meanwhile, the secondary transmitter (BD) initiates information transmission with the aid of transmission behavior of Alice to avoid being detected by Willie, who transmits artificial noise (AN) to interfere with legitimate communication. Then, the minimum detection error probability (DEP) as well as the corresponding optimal threshold has been obtained. Moreover, the optimization problem of maximizing covert rate has been presented and addressed by utilizing a hybrid grid-gradient optimization algorithm. The results indicate the superiority of the proposed probabilistic covert transmission scheme in terms of covert rate and covert energy efficiency (CEE).

Federated Learning (FL) faces critical challenges in heterogeneous and resource-constrained environments, including device diversity, high communication overhead, and training delays. Therefore, we propose FedKDC, a federated learning framework that integrates knowledge distillation with data compression to jointly optimize server bandwidth, client computation resources, and compression ratios, thereby minimizing training latency. In particular, FedKDC employs a Generative Adversarial Network (GAN)-based generator to produce synthetic data for knowledge transfer across heterogeneous models without sharing raw data, mitigating privacy risks. Then, FedKDC uses a loss-driven adaptive compression mechanism to adjust the minimum compression threshold based on training stability, reducing communication volume while maintaining accuracy. In addition, we further discuss the problem of resource allocation under system constraints, and uses Particle Swarm Optimization (PSO) algorithm to solve it. Based on the three real world datasets (i.e., Fashion-MNIST, CIFAR-10, and CIFAR-100), the experimental results demonstrate that FedKDC reduces communication cost by up to 17% and training time by 8%. This shows that FedKDC is effective for large-scale heterogeneous FL deployment while maintaining the accuracy of the model.

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).

As a promising methodology of distributed Machine Learning (ML) paradigm, Federated Learning (FL) protects data privacy and reduces communication cost by aggregating model parameters rather than raw data. However, training superb FL models incurs a lot of energy consumption, which is a significant challenge for energy-limited Mobile Devices (MDs). To address this challenge, this paper proposes a Wireless Power Transfer (WPT)-aided FL framework, where MDs train local FL models for Base Station (BS) and get corresponding payoff, while Wireless Charge Provider (WCP) provides energy supplement for MDs and charges energy fees. Furthermore, we take into account the time-varying nature of MDs datasets, which affects their energy consumption and reward from BS. Then, we formulate the investigated problem to achieve joint optimization of WPT duration, computing resource allocation and the number of local iterations, with the goal of maximizing the total utility of all MDs throughout the whole FL process. The optimization problem is NP-hard and difficult to be solved by traditional optimization methods within limited timeframes. Therefore, we use Karush-Kuhn-Tucker (KKT) conditions and Lagrange dual method to analyze the problem, and propose a new Improved Lagrangian Subgradient Method (ILSM) as an efficient solution. Finally, extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme under various scenarios, and the results show that the proposed ILSM significantly outperforms other benchmarks in terms of the total utility of all MDs.

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.

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios.

This paper proposes a Stacklberg game-based Dependent task Offloading and resource Pricing framework (SDOP), where vehicles partially offload their dependent substaks to the SDN controller and pays corresponding fees. Firstly, we model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG outperforms other baseline schemes under various scenarios.

This paper investigates dynamic model partitioning and resource allocation in split federated learning, aiming to maximize the utility of clients and the Central Server (CS). We first model the interactions between the CS and clients as a Stackelberg game, where the CS acts as the leader to set payment and allocate computation resources, while clients as followers to determine model partitioning strategies. Then, we transform the problem into a bi-level optimization and propose a Nash-Equilibrium-based Stackelberg Algorithm (NESA) to solve it. Finally, the experimental results indicate that a Stackelberg equilibrium exists between the CS and clients, and NESA achieves higher utility and improves accuracy and convergence speed.

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users (MUs)' tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks increase, a single UAV is difficult to meet all computational demands, result in the decline of QoS. To address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without charging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the highest overall system utility under different scenarios.

Vehicular edge computing (VEC) is an effective paradigm in Internet of Vehicles (IoV), which allows vehicles to offload delay-sensitive tasks to nearby road side units (RSUs) for processing, thereby enhancing the Quality of Service (QoS). However, the software defined networking (SDN) controller that manages RSUs often have individual rationality and selfishness, and thus is unwilling to provide free computation resources to vehicles. Meanwhile, the dependency relationships among vehicular subtasks are not well investigated, resulting in unsatisfactory task latency and energy consumption. In order to effectively motivate the selfish SDN controller to participate in computation offloading and comprehensively consider all dependency situations among multiple subtasks, this article proposes a Stackelberg game-based dependency-aware task offloading and resource pricing framework (SDOP). Specifically, we first model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a gradient ascent plus genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios, when compared with other baseline schemes.

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks.

This paper considers the joint optimization of computation offloading, service caching, and resource allocation in the Digital Twin Edge Network (DTEN), and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP), whose goal is to minimize the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and outperform other benchmark algorithms under different scenarios.