Data heterogeneity caused by Non-Independent and Identically Distributed (non-IID) data in local clients imposes limitations and challenges on the training and performance of Federated Learning. Current researches on this issue mainly focus on optimizing a single global model or developing personalized model for each client, neglecting the essential client relationships. However, despite the heterogeneity of client data, common characteristics that can be leveraged still exist. Therefore, We proposed a novel personalized federated learning approach, called pFedBEA. The method decomposes the client model into a body model (extractor) and a head model (classifier) to respectively adapt to common features and personalized attributes. Subsequently, periodically abandoning the global server aggregation and exchanging the body models among different clients, which enables local personalization while learning from multiple data sources, promoting knowledge sharing and enhancing the generalization ability of the global model. pFedBEA not only improves model performance but also reduces the number of aggregation rounds and communication time, achieving overall efficiency optimization. We conducted extensive experiments on a range of datasets, demonstrating that pFedBEA achieves higher accuracy, superior aggregation efficiency and communication efficiency.

Federated learning enables collaborative training across various clients without data exposure. However, data heterogeneity among clients may degrade system performance. The divergent training goals of servers and clients lead to performance degradation: servers aim for a global model with improved generalization across all data, whereas clients seek to develop private models tailored to their specific local data distributions. This paper introduces a novel federated learning framework named FedAMKD. FedAMKD divides federated learning into two independent entities, a local model tailored to each client's data and a global model for data aggregation and knowledge sharing. A unique aspect of FedAMKD is its adaptive mutual knowledge distillation at the local level, customized for the skewed degree of the client's data quantity. This method achieves the goal of enhancing both local and global model performance, reducing the adverse effects of data quantity-skewed heterogeneity in federated learning. Extensive experiments across diverse datasets validate FedAMKD's success in addressing challenges related to data quantity imbalances in federated learning,

Abstract Artificial Intelligence of Things (AIoT) is a new research area in AI and IoT. For the massive data and connections at the edge of AIoT, how to schedule the resource load is a key problem to be solved. For the multi‐objective resource scheduling problem in a cross‐domain environment, this article proposes a resource scheduling method based on edge computing and multi‐objective algorithms. First, a cloud‐edge hybrid AIoT hierarchical network resource management architecture is constructed. Then, the resource scheduling problem is modeled and a linear weighting strategy is designed for search space “pruning”, which is then optimized by three multi‐objective algorithms. Finally, we conducted a series of experiments based on the iFogSim platform. The simulation results show that the proposed resource scheduling strategy can effectively reduce the processing delay of AIoT and effectively improve the energy utilization efficiency of devices in the network.

Abstract Blockchain has decentralization characteristics and requires more targeted security schemes to protect user privacy. In contrast, existing signature schemes have many high‐complexity operations and impose an enormous computational burden on wireless nodes. This article proposes a light‐weighted identity privacy scheme for blockchain‐based on edge computing. We construct linkable identity privacy and non‐linkable identity privacy, which can resist collusion attacks while virtually guaranteeing blockchain nodes' identity privacy. Since edge computing offloads heavily, the proposed scheme has lower computational complexity than the existing techniques.

Summary With the development of information technology, the Internet of Things terminals are vulnerable to threats such as eavesdropping, tampering, and counterfeiting. The application of threshold group signature technology can effectively ensure the security of the system in applications such as battlefield intelligent decision‐making, connected vehicles, and intelligent manufacturing. However, the existing threshold group signature schemes lack the two‐way trusted authentication mechanism between group members and group manager, and group manager can easily become a security risk for the entire signature system. To solve this problem, this paper proposes a threshold group signature scheme based on elliptic curve that achieves mutual authentication of group members and group manager. The security analysis proves that the proposed scheme is anonymous, traceable, and can resist collusion attacks and frame attacks. The results of performance analysis and comparison also show that under the same security strength, the scheme proposed has shorter signature length, lower calculation amount for signature generation and signature verification, which can effectively reduce the communication and calculation overhead of the IoT terminal, and can be better applied to the Internet of Things application scenarios.

Summary Regional population density has temporal and spatial characteristics, and most of the existing prediction models fail to take these two characteristics into account at the same time, which results in unsatisfactory forecasting results. To address this problem, we use the deep learning models to predict the crowd distribution in the evacuation area, so as to realize the recommendation of the evacuation area. First, a raster population density prediction model based on long short‐term memory (LSTM) is studied, and then a multiarea population density prediction model considering temporal and spatial characteristics, named ST‐LSTM, is designed. The results of our extensive experiments on the real dataset show that our proposed ST‐LSTM is both effective and efficient.

Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing whole user information will result in a low recommendation efficiency. In addition, the traditional recommendation system ignores the inherent connection between user's preference and time. In reality, the interest of the user varies over time. Recommendation system should provide users accurate and fast with the change of time. To address this, we propose a novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-means), called TCCF. The clustering method can cluster similar users together for further quick and accurate recommendation. Moreover, an effective and personalized recommendation model based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It can provide a higher quality recommendation by analyzing the user's behaviors. The extensive experiments are conducted on two real datasets of MovieLens and Douban, and the precision of our model have improved about 5.2 percent compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.

Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for IoT is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model, including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios is realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication are realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.