The integration of artificial intelligence and edge computing has given rise to edge intelligence. Federated Learning (FL), as a key technology of edge intelligence, utilizes data and computation power on edge devices to achieve decentralized edge intelligence. The problem of incentive mechanism design and resource allocation in FL has been two key issues that hinder the smooth progress of the learning process. We study an edge intelligence network that supports multiple simultaneous FL services, and there are multiple task publishers seeking FL services and mobile device alliances providing services for them. We address these two key issues. First, how task publishers can incentivize device alliances to contribute resources to facilitate the execution of FL services. Second, how the device alliances allocate resources across multiple FL services that are in progress to enable bounded rational decision-making. In order to solve the former, we transform the incentive design under information asymmetry into a contract game problem. For the latter, we propose an evolutionary game model to adjust their resource allocation strategies, while considering the influence of bounded rationality and external factors on the decision-making of device alliances. The simulation results show that our incentive mechanism shows excellent performance in the case of information asymmetry. Additionally, We experimentally simulated the dynamic evolution of device alliance strategies.

Unmanned aerial vehicles (UAVs) equipped with high definition (HD) cameras, intelligent sensors, computing, and communication modules can be deployed to execute crowdsensing tasks by leveraging federated learning (FL), e.g., air quality perception and ground target detection. FL can reduce transmission stress and protect data privacy when training models, which is suitable for resource constrained Internet of UAVs. Nevertheless, the incentive issues about information asymmetry and bounded rationality impede the applications of FL-enabled Internet of UAVs. The existing FL incentive approaches focus on the risk-free condition, where task publishers are capable of making decisions with complete rationality by utilizing expected utility theory. In fact, task publishers under risk conditions are often bounded rational, whose risk-awareness makes the utility models more sophisticated. To overcome the above problems, we present a prospect theory (PT)-based incentive mechanism for FL-enabled Internet of UAVs. We first leverage PT to model the task publisher’s risk-awareness behavior and construct the subjective utility model. Thereafter, we utilize the framing effect of PT to design the optimal contract to maximize the subjective utility. Simulation results demonstrate that, compared with the baseline method, the proposed incentive mechanism has better performance.

As an emerging technology, federated learning (FL) plays a critical role for information sharing in industrial Internet of Things (IIoT). FL integrates information from multiple devices to collaboratively train a joint machine learning model locally without sharing the individual training data. Most existing incentive mechanism schemes for FL assume that the task publisher is completely rational and capable of making decisions based on expected utility theory (EUT). However, in reality, the task publisher is characterized as bounded rationality under risks and uncertainties, whose risk-awareness makes EUT inapplicable when making decisions. To tackle the above challenge, a novel incentive mechanism for IIoT-FL based on contract theory and prospect theory is proposed in this paper. We leverage prospect theory to model the task publisher’s risk-awareness behavior. To guarantee high model accuracy while avoiding serious time delay, we take the global model quality and time satisfactory into account when designing the optimal contract. Simulation results demonstrate that our incentive mechanism is effective under asymmetric information and risk.

Cross-silo federated learning is a new type of machine learning, where organizations, as model owners, collaborating to train global models with local data. Nevertheless, the heterogeneity caused by differences in training costs within the organization leads to different views on training rounds among different organizations. At the same time, the organizations’ communication resources are non-excludable public goods, resulting in free-rider problems. To address the aforementioned concerns, we propose a cross-silo federated learning incentive mechanism based on a consortium blockchain. The consortium blockchain coordinates the training rounds and currency transfers, as well as determines the processing capabilities of each organization’s local training, and helps organizations record the training models during the training process. The mechanism models the game between organizations as non-cooperative games. We conducted a theoretical analysis of the mechanism using the MNIST dataset and demonstrated the existence of Nash equilibrium. A distributed algorithm has been proposed to maximize social welfare for organizations under information asymmetry conditions. The simulation results show that the algorithm has fast convergence.

Federated edge learning (FEEL) is gaining increasing attention due to its characteristics of privacy protection, low latency, and low communication overhead. However, it still faces challenges such as a single point of failure and the imbalance between communication efficiency and training efficiency caused by heterogeneous clients. To address the above issues, we investigate a semi-decentralized FEEL (SD-FEEL) architecture, where edge servers train their local models with the associated clients in a centralized manner and exchange models with their one-hop neighbors distributively. For the upper-layer edge servers, a fully asynchronous aggregation mechanism is proposed to accelerate the model diffusion, while for the lower-layer clients, the proximal policy optimization (PPO) algorithm is employed to dynamically select the aggregation time based on currently available resources. Simulation results show that the proposed algorithm can effectively balance training efficiency and communication efficiency. Besides, the model accuracy of the proposed algorithm is improved by around 4.5% and 11% compared to asynchronous FL and synchronous FL, respectively.

Live traffic video is vitally important for vehicles in future intelligent transport systems (ITSs). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this article, we propose a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs)-assisted ITS, in which UAVs are considered as the eyes in the sky to capture real-time traffic videos. We aim to maximize the long-term video quality received by vehicles by jointly optimizing vehicle grouping and spectrum allocation. Considering the interactions among UAVs, the above optimization problem is formulated as a multiagent coordination problem in the form of a Markov game (MG). The MG is subsequently solved by leveraging a state-of-the-art multiagent deep reinforcement learning (MADRL) algorithm, namely, multiactor attention critic (MAAC), in which an attention mechanism is utilized to pay attention to other agents to make the learning process more effective and scalable. Extensive simulation results show that the MAAC-based algorithm has better performance in terms of video quality and spectrum efficiency compared with the baseline methods.

With the rapid development of automotive industry and telecommunication technologies, live streaming services in the Internet of Vehicles (IoV) play an even more crucial role in vehicular infotainment systems. However, it is a big challenge to provide a high quality, low latency, and low bitrate variance live streaming service for vehicles due to the dynamic properties of wireless resources and channels of IoV. To solve this challenge, we propose a novel live video transcoding and streaming scheme that maximizes the video bitrate and decreases time-delays and bitrate variations in vehicular fog-computing (VFC)-enabled IoV, by jointly optimizing vehicle scheduling, bitrate selection, and computational/spectrum resource allocation. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels of IoV. A soft actor–critic deep reinforcement learning (DRL) algorithm that is based on the maximum entropy framework, is subsequently utilized to solve the above MDP. Extensive simulation results based on the data set of the real world show that compared to other baseline algorithms, the proposed scheme can effectively improve video quality while decreasing latency and bitrate variations, and access excellent performance in terms of learning speed and stability.

Unmanned aerial vehicles (UAVs) are anticipated to be integrated into the next generation wireless networks as new aerial mobile users, which can provide various live streaming applications such as surveillance, reconnaissance, etc. For such applications, due to the dynamic characteristics of traffic and wireless channels, how to guarantee the quality of service (QoS) is a challenging task. In this paper, with recent advances in scalable video coding (SVC), we study secure video streaming in wireless networks with UAVs. By jointly optimizing video levels selection and power allocation, the research tries to maximize the energy efficiency, which is the ratio of video quality to power consumption, while satisfying the secrecy timeout probability (STP) requirement. The aforementioned problem is modeled as a constrained Markov decision process (CMDP). And then, the study employs a state-of-the-art reinforcement learning algorithm, namely safe deep Q-learning network (safe-DQN), to solve the CMDP problem, in which a safety policies set is induced by constructing a Lyapunov function. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm compared with other existing reinforcement learning algorithms.

Offloading computationally intensive tasks from user equipments (UEs) to mobile edge computing (MEC) servers is a promising technique to boost up the computational capacity of UEs. However, MEC will incur extra energy consumption and time delays, which motivates the deployment of energy harvesting (EH) small cell networks with MEC in mobile networks. Due to the complexity of such networks, it is challenging to effectively allocate resources for UEs. In this paper, we investigate the offloading decision, wireless and computational resources allocation problem in energy harvesting (EH) small cell networks with MEC. Different from existing literatures, our research focuses on improving mobile operators' revenue by maximizing the amount of the offloaded tasks while decreasing the energy expenditure and time-delays. Besides, queues are created at the MEC server side to store the un-executed tasks in a time slot, which is used as a punishment in our utility function to avoid serious delay. Considering the varying lengths of queues, the states of EH-batteries of small base stations (SBSs) and down-link channels, the above problem is modeled as a Markov decision process (MDP). Since the states and actions in the MDP are infinite, an online and on-policy actor-critic with eligibility traces algorithm is proposed to resolve the problem. Simulation results show the proposed algorithm has superior performances compared with the policy-gradient algorithm and Q-learning.