Federated learning emerges as a solution to the dilemma of data silos while safeguarding data privacy, particularly relevant in the consumer electronics sector where user data privacy is paramount. However, federated learning is generally employed in a heterogeneous scenario, consisting of various factors that influence the training efficiency and accuracy of the federated learning models. There are many classic references focusing on federated communications, federated robustness and federated fairness, conversely, few of them clarify and summary systematically the influence of heterogeneity on the effect of federated learning. Therefore, we provide an overview of three heterogeneous challenges faced by federated learning in practical applications: device heterogeneity, data heterogeneity and model heterogeneity, and analyze their influence on federated learning. This is especially crucial in consumer electronics, where heterogeneity directly influence the performance and user experience of AI-driven features. And then, we highlight current solutions, ideas and challenges to compare different strategies for facing heterogeneous problems and outline several directions of future work that are relevant to a wide range of research communities.

Now public transportation, included bus and subway occupies a greater role in city transport, and the layout of traffic stations is the most important part of planning and design. However, some unpredicted factors for construction of traffic stations results in a low utilization rate of public transportation resources, for example, the layout of bus stops is chaotic, there is no clear layout scope, and there is a lack of integration with residents’ travel hotspots. Towards these challenges modern transport faces, we firstly analyze the distribution of bus stops and subway stations to determine the area range needs to be optimized in the traffic net from the perspective of time and space. And then, we propose an optimization method, called ’partial area clustering’ (PAC), to improve the utilization by changing and renewing the original distribution. The novel method was based on the K-means algorithm in the field of machine learning. PAC worked to search the suitable bus platforms as the center and modified the original one to the subway. Experiment has shown that the use of public transport resources has increased by 20%. The study uses a similar cluster algorithm to solve transport networks’ problems in a novel but practical term. As a result, the PAC is expected to be used extensively in the transportation system construction process.

Modern traffic management must balance the ability to predict complex, non-stationary flow patterns with the strict enforcement of safety and legal constraints (e.g., minimum pedestrian intervals, maximum cycle lengths). While generative models such as Variational Autoencoders (VAEs) can effectively capture traffic dynamics, purely data-driven approaches often fail to respect domain-specific rules. We propose a Neuro-VAE-Symbolic framework that combines the flexibility of VAE-based generation with the rule-enforcing capability of symbolic reasoning. By projecting raw neural outputs onto a feasible action space defined by traffic regulations, our method ensures both adaptability to dynamic demand and strict constraint compliance. Experiments on benchmark datasets show that our method reduces average waiting time by up to 15%, cuts queue lengths by 10–20%, and increases throughput by 5–8%, all while maintaining near-zero rule violations. These results demonstrate that neuro-symbolic integration enables high-fidelity traffic scenario generation alongside reliable, regulation-abiding decision-making, offering a robust solution for dynamic urban traffic control.

Federated learning still faces many problems from research to technology implementation and the most critical problem is that the communication efficiency is not high. Therefore, the main task of this work is to improve the communication efficiency, affected by communication devices, bandwidth, data distribution and so on. Our work focus on two main aspects of federated learning — updates strategy and data heterogeneity, and propose a new federated framework called TKAGFL, which is composed by three key factors. Firstly, in response to the problem of data heterogeneity that often occurs in actual federated learning, conditional generative adversarial networks (GANs) are proposed as a method of data preprocessing. Secondly, the improved homomorphic encryption method counterbalances the conflicts between data-sharing and privacy protection. Thirdly, in the communication process, classic AdaGrad optimization in deep learning and top-K algorithms are combined to compress communication parameters to improve communication efficiency. The results of experiments demonstrate our TKAGFL framework's accuracy is about 15% ~ 20% better than other algorithms or frameworks and it can converge at 150 communication rounds and is 50 rounds faster than others. Also, our TKAGFL algorithm reduces 10 times communication volume than other algorithms,which is beneficial for federated learning application in industry.

The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people’s lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.