As the demand for computation-intensive and lowlatency services grows, mobile edge computing (MEC) has been widely applied in smart devices to provide efficient and real-time assistance. However, most existing studies impose fixed assumptions and lack consideration for the uncertainty within MEC. This makes it difficult for these studies to reasonably offload tasks in complex and highly volatile scenarios. Therefore, we construct an MEC task offloading system considering multifactorial uncertainty (MECTOS-MU), which involves multiple devices and MEC servers (MSs). In MECTOS-MU, task offloading and resource allocation are jointly optimized while complying with the constraint on latency to minimize the energy consumption of all devices, which is an NP-hard problem. To address this issue, we propose a novel algorithm called distributed game offloading based on load balancing (DGOLB). This method integrates task offloading prioritization, static game theory, and load balancing to formulate efficient task offloading decisions and resource allocation schemes. Extensive simulation results demonstrate that DGOLB outperforms other baseline algorithms in terms of energy consumption, ratio of dropped tasks, and average task response time, especially in scenarios with a large number of devices.

While Federated Learning is intended to safeguard data privacy, it is confronted with the problem of gradient leakage, which empowers attackers to execute gradient inversion attacks and retrieve the original data through the shared gradient information. Existing gradient inversion attack methods can achieve good results when handling small batches of low-resolution images. However, when dealing with large batches of high-resolution images, problems such as gradient ambiguity and model instability will occur, resulting in a significant decrease in the recovery performance. We propose a novel Image-correction-penalty based Over-parameterized Regression Network (IORN). IORN breaks through the limitations of existing methods with its unique design. The Adaptive Over-parameterized Network in IORN can dynamically adjust its structure, thereby enhancing the network’s ability to capture complex data distributions. This enables it to better handle the complexity of large batches of high-resolution images and improves the model’s reconstruction ability for such images. Meanwhile, the designed image correction penalty term restricts the difference between the generated images and the average image. This not only improves the stability of the optimization process but also reduces the convergence deviation. Experimental results demonstrate that IORN significantly improves the resolution and fidelity of reconstructed images during gradient inversion attacks on the MNIST, CIFAR-100, and LFW datasets, especially showing outstanding performance when dealing with large batches of complex images.

In a fully heterogeneous federated learning environment, the client has significant differences in model structure and local data distribution (Non-IID), and the joint learning of the client model is blocked due to the limited communication content available for interaction in a fully heterogeneous scenario. In this context, the global knowledge constructed by the server through the simple aggregation of the client logits is essentially a fuzzy representation containing a lot of noise and information loss, which is difficult to effectively guide the client model update. To solve these problems, this paper proposes a heterogeneous federated learning framework (FedMkd) based on multi-knowledge distillation fusion to cope with multiple challenges in heterogeneous environments. The FedMkd framework uses a class-grained logits interaction architecture (CLIA) and introduces an efficient knowledge sharing mechanism. It innovatively integrates two knowledge distillation methods: 1) Temperature-Adaptive Knowledge Distillation (TAKD), which provides differentiated temperatures for teacher and student models by adaptively adjusting the distillation temperature, maximizing knowledge transfer between them; 2) Class-related Knowledge Distillation (CRKD), which introduces batch-level sample correlation loss to reduce over-reliance on specific samples or classes and improve the model's understanding of overall data features. We conducted a large number of experiments on four public data sets. The results show that in a variety of data and model heterogeneous scenarios, FedMkd still performs better than the comparison method when the communication overhead is reduced by more than one order of magnitude.

Aiming at the problems of computing power, reliability and cost when intelligent vehicles deal with computationally intensive and delay-sensitive emerging applications in multiple business scenarios in the Internet of Vehicles, an intelligent computing offloading model is proposed. This can minimize the total system cost under the constraint of time delay and energy consumption. Considering the cost of blockchain and the cost of intelligent vehicles, the DDPG algorithm is used to solve the proposed model. Simulation results show that the method proposed in this paper can effectively reduce the total cost of computing offload and further improve the success rate of computational offloading under the premise of computational offloading safety.

With the rapid development of society and the economy and the increasing seriousness of environmental problems, renewable energy and high-quality energy services in low-carbon communities have become popular research topics. However, a large number of volatile distributed generation power systems in the community are connected to the grid. It is difficult to stabilize and efficiently interact with fragmented and isolated energy management systems, and it is difficult to meet energy management needs in terms of low-carbon emissions, stability, and intelligence. Therefore, by considering operation costs, pollution control costs, energy stability, and plug-in hybrid electric vehicles, this article proposes a regional energy supply model called community energy Internet and builds a low-carbon community energy adaptive management model for smart services. Then, to address energy supply instability, an adaptive feedback control mechanism developed based on model predictive control is introduced to adapt to the changing environment. Finally, a long short-term memory-recurrent neural network-based Tabu search is introduced to prevent the multiobjective particle swarm optimization algorithm from easily falling into a local optimum. The simulation results show that the proposed model can effectively realize the optimal allocation of energy, which solves the problem of fragmented energy islands caused by distributed power access. This method has quality of service benefits for users, such as cost, time, and stability, and realizes wide interconnections, high intelligence, and low-carbon efficiency of community energy management.

Rapid economic development has been observed worldwide, which has caused environmental problems to worsen. Thus, the Energy Internet (EI), which accesses renewable energy and provides high-quality power services, has recently become a hot issue. As a subnet of the EI, an energy local area network (ELAN) consists of renewable power generation equipment, controllable distributed power generation equipment, storage systems, electric vehicles, and a large number of loads. Energy management is required for economic, environmental, and safety considerations. This paper proposes an energy management optimization model that addresses ELAN operations and includes pollution treatment fees; this model provides intelligent control of the charging and discharging of plug-in hybrid electric vehicles (PHEVs). This model achieves a nonlinear energy management optimization for an ELAN. To promote optimal performance, an improved comprehensive learning particle swarm optimization (CLPSO) algorithm is presented; it combines Tabu Search (TS) and CLPSO to avoid local optima. To verify the performance of our model, two experimental scenarios are built. The simulation results show that our energy management optimization model fulfills the optimal allocation of energy and that the PHEV intelligent charging/discharging strategy promotes economic benefits for the network.