The effectiveness of federated learning (FL) systems relies on extensive client participation. However, the existing incentive mechanisms often overlook the intrinsic coupling between participation and privacy strategies. This oversight leads to mismatches between incentive distributions and clients’ privacy costs and contribution quality, limiting the effectiveness of incentives for enhancing contribution quality and participation scale. To address this, DIM-NEG, a dynamic incentive model based on smart contracts and nested evolutionary games, is proposed. First, we construct a nested evolutionary game framework linking external participation with internal privacy strategies. By utilizing the internal equilibrium payoff as a feedback parameter for the external game, we achieve unified modeling of these coupled decision processes. Second, on the basis of this structure, a functionally decoupled dual-incentive lever mechanism is proposed. The server employs the strategy incentive to guide the internal strategy portfolio and the participation incentive to regulate the participation scale, enabling separate optimization of the strategy composition and overall participation level of the system via hierarchical control. Finally, utilizing blockchain-based smart contracts, we design an automated mechanism that encodes rules on-chain to resolve trust issues associated with centralized servers. A theoretical analysis and simulation results demonstrate that the DIM-NEG model achieves superior global accuracy, training efficiency, and communication cost-effectiveness, while exhibiting strong robustness in non-IID environments. The model adequately motivates users to participate in high-quality data sharing tasks and maintains system stability, thereby maximizing the overall effectiveness of the federated learning system.

Mobile edge computing (MEC) enables various services to be cached in close proximity to the user equipments (UEs), thereby reducing the service delay of many emerging applications. However, the limitation of storage, computation, and radio resources, the dynamics of the decentralized MEC environment, and the complex spatial relationships of service request types and wireless network states between edge nodes make it difficult to realize efficient edge computing services. To address these challenges, this paper integrates the digital twin (DT) technology with a multi-cell MEC network to study an intelligent cooperative task offloading and service caching scheme, aiming at maximizing a quality of services (QoE)-based system utility. Specifically, we first construct a digital twin edge network (DITEN) to reflect the physical MEC system in real-time and provide data for training. With the help of DT technology, it is easy to access data resources in the DITEN to improve the simulation ability and reduce the communication cost. Then, we propose a graph attention-based multi-agent reinforcement learning (GatMARL) algorithm to learn the optimal task offloading and service caching strategies in the DITEN. The GatMARL employs a graph attention-based value decomposition network to capture the potential spatial relationships between edge nodes to learn better attentive cooperation policy. Simulation results demonstrate that the proposed GatMARL algorithm exhibits an effective performance improvement compared with state-of-the-art benchmarks.

Accurate spatial-temporal traffic modeling and prediction play an important role in intelligent transportation systems (ITS). Recently, various deep learning methods such as graph convolutional networks (GCNs) and recurrent neural networks (RNNs) have been widely adopted in traffic prediction tasks to extract spatial-temporal dependencies based on a large volume of high-quality training data. However, there exist data scarcity problems in some transportation networks, and in these cases, the performance of traditional GCNs and RNNs based approaches will degrade sharply. To address this problem, this paper proposes an adversarial domain adaptation with spatial-temporal graph convolutional network (Ada-STGCN) model to predict traffic indicators for a data-scarce target road network by transferring the knowledge from a data-sufficient source road network. Specifically, Ada-STGCN first develops a spatial-temporal graph convolutional network that combines the GCN and gated recurrent unit (GRU) to extract spatial-temporal dependencies from source and target road networks. Then, the technique of adversarial domain adaptation is integrated with the spatial-temporal graph convolutional network to learn discriminative and domain-invariant features to facilitate knowledge transfer. Experimental results on the real-world traffic datasets in the traffic flow prediction task demonstrate that our model yields the best prediction performance compared to state-of-the-art baseline methods.

Mobile edge computing (MEC) enables various services to be cached in close proximity to the user equipments (UEs), thereby reducing the computing delay of many emerging applications. Nevertheless, The limited storage capacity of edge servers requires judicious design of service caching as well as task offloading to maximize edge computing performances. In this paper, we formulate a cooperative task offloading, service caching, and transmit power allocation problem to minimize the cost of computing delay and energy consumption of UEs. To address this problem, we propose a graph attention based multi-agent deep deterministic policy gradient (GAT-MADDPG) algorithm, in which a multi-headed graph attention mechanism is incorporated into the centralized critic network to learn the attentive cooperation policies. Simulation results show that the proposed GAT-MADDPG algorithm exhibits an effective performance improvement.

With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm.

It is still an open challenge in coding theory how to design a systematic linear ( n , k ) − code C over GF(2) with maximal minimum distance d . In this study, based on matroid theory (MT), a limited class of good systematic binary linear codes ( n , k , d ) is constructed, where n = 2 k − 1 + · · · + 2 k − δ and d = 2 k − 2 + · · · + 2 k − δ − 1 for k ≥ 4, 1 ≤ δ < k . These codes are well known as special cases of codes constructed by Solomon and Stiffler (SS) back in 1960s. Furthermore, a new shortening method is presented. By shortening the optimal codes, we can design new kinds of good systematic binary linear codes with parameters n = 2 k − 1 + · · · + 2 k − δ − 3 u and d = 2 k − 2 + · · · + 2 k − δ − 1 − 2 u for 2 ≤ u ≤ 4, 2 ≤ δ < k . The advantage of MT over the original SS construction is that it has an advantage in yielding generator matrix on systematic form. In addition, the dual code C ⊥ with relative high rate and optimal minimum distance can be obtained easily in this study.