Federated learning (FL) has emerged as a compelling distributed learning paradigm without sharing local original data. However, with ubiquitous non-independent and identically distributed (non-IID) privacy data, the FL suffers from severe performance loss and the privacy leakage by inference attacks. Existing solutions lack a cohesive framework with theoretical support, and their performance optimization and privacy protection are inter-inhibitive or high-cost. In this paper, we propose a digital twin (DT)-empowered federated incremental learning method to tackle the above challenges. First, we construct a DT-empowered federated incremental learning model to achieve cooperative awareness of performance and privacy-preservation. Second, a diffusion model-based selective data synthesis method is designed to provide auxiliary data for FL, it can avoid unnecessary overhead while ensuring the quality of synthetic samples under non-IID. Besides, it alleviates the negative impact of non-IID by allocating a class-balanced sub-dataset to each DT with IID setting. Third, we develop a DT-empowered alternating incremental learning method initiatively, under the premise of ensuring the confidentiality of original dataset, it can achieve efficient FL performance under non-IID with a small amount of synthetic samples. Furthermore, in order to estimate the contribution of each local model accurately, we investigate a comentropy-based federated aggregation strategy, which can obtain a superior global model. By sufficient theoretical analysis, we prove that the proposed methodology can achieve consistent enhancement of performance and privacy-preservation. Simultaneously, the experiments demonstrate that our methodology has efficient privacy-preserving property, it also outperforms other benchmarks on the accuracy and stability of the global model, especially in highly heterogeneous scenarios.

In the Industrial Internet of Things (IIoT) environment, there is a multitude of heterogeneous industrial edge devices (IEDs) from various sources. Real-time monitoring and precise prediction of its operational status are typically essential. However, existing deep learning-based models often encounter overfitting issues due to complex parameter configurations. Furthermore, ensuring the comprehensive performance of anomaly event forecasts for IEDs has emerged as a pressing issue requiring resolution to accommodate a wider range of practical applications. In this article, we introduce a novel multibranch long short term memory and differential overfitting mitigation scheme (MuLDOM). This scheme is designed to achieve two primary objectives: 1) to extract features and denoise multivariate time series adaptively and 2) to implement the differential overfitting mitigation algorithm for the first time, thereby enabling robust intelligent anomaly detection and forecast (IADF). Expanding on this framework, we provide detailed information on the development of an online prediction scoring mechanism based on multivariate time series data. This mechanism aims to enhance the efficiency of quantitatively estimating the spatial and temporal characteristics associated with IEDs. We conducted extensive experiments on four publicly available industrial data sets and compared our approach with nine recent baseline methods. The results indicate that our method surpasses the recent state-of-the-art methods, validating its effectiveness. These findings underscore its significant potential for real-world applications.

Federated learning (FL) gets a sound momentum of growth, which is widely applied to train model in the distributed scenario. However, huge communication cost, poor performance under heterogeneous datasets and models, and emerging privacy leakage are major problems of FL. In this paper, we propose a communication-efficient personalized FL scheme with privacy-preserving. Firstly, we develop a personalized FL with feature fusion-based mutual-learning, which can achieve communication-efficient and personalized learning by training the shared model, private model and fusion model reciprocally on the client. Specifically, only the shared model is shared with global model to reduce communication cost, the private model can be personalized, and the fusion model can fuse the local and global knowledge adaptively in different stages. Secondly, to further reduce the communication cost and enhance the privacy of gradients, we design a privacy-preserving method with gradient compression. In this method, we construct a chaotic encrypted cyclic measurement matrix, which can achieve well privacy protection and lightweight compression. Moreover, we present a sparsity-based adaptive iterative hard threshold algorithm to improve the flexibility and reconstruction performance. Finally, we perform extensive experiments on different datasets and models, and the results show that our scheme achieves more competitive results than other benchmarks on model performance and privacy.

This paper focuses on the security issue in Cyber-Physical Systems. The sensor transmits the state estimation information to the remote controller via wireless networks. Due to the broadcast characteristics of wireless communication, the systems are vulnerable to the eavesdropping attacks and jamming attacks. In this paper, we study how to maximize the secure transmission rate between sensors and controller with the presence of malicious eavesdropper and jammer. The malicious jammer is smart and can choose the optimal power strategy to maximize the side effect with the knowledge of sensor's transmission power. When the sensor adjusts its transmission power to achieve the maximum utility, the control feedback is used to adjust the optimal strategy in CPS. We formulate this proposed problem as a Stackelberg game such that the optimal power allocation strategy is achieved. We further prove the existence of Stackelberg equilibrium by obtaining the interaction between the sensor and the jammer. To tackle this optimization problem, we present a stochastic algorithm with feedback (SAF) algorithm. As a result, the optimal transmission strategy is obtained. Finally, extensive simulations are presented to verify our theoretical analysis.

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation of sensor readings has become a research focus. In this paper, we propose a hierarchical network framework and adaptive threshold compression scheme to reduce the amount of information transmissions and alleviate the network congestion by exploring the spatial correlation among signals. The adaptive spatial compression scheme can obtain higher reconstruction precision by selectively discarding the less significant elements. Meanwhile, the compression ratio varies with the correlation among signals and adaptive threshold, so our scheme is adaptive to various deployed environments. Finally, the simulation results confirm that the proposed scheme achieves higher reconstruction precision and compression gain as compared with other spatial compression scheme.

Since the temporal and spatial correlations of sensor readings are existent in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC) and compressed sensing (CS) for correlated data. The proper selections of NC coefficients and measurement matrix are designed for this scheme. This design guarantees the reconstruction of clustered compression data successfully with an overwhelming probability and unifies the operations of NC and CS into real field successfully. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing the independent encoding in each sensor node (including the cluster head nodes) and joint decoding in sink node. At the same time it has lower computational complexity as compared with JSM-based spatio-temporal scheme by exploiting the temporal and spatial correlations of original sensing data step by step. Finally, the simulation results verify that the clustered spatio-temporal compression scheme outperforms the other two compression schemes significantly in terms of recovery error and compression gain.

This paper presents an improved routing algorithm based on the social link awareness. In this algorithm, multiple social features of the nodes' behaviors are utilized to quantify the nodes pairs' social links. The social links of the nodes pairs are computed based on their encounter history. These social links can be used to construct the friendship communities of the nodes. The intra-community and inter-community forwarding mechanisms are implemented to raise the successful delivery ratio with low overhead and decrease the transmission delay. Simulation results show that the proposed algorithm shortens the routing delay and the overhead, and increases the successful delivery ratio, thereby improving the routing efficiency.

This paper minimizes the routing security risk while limiting the delivery ratio under an ideal value by 1) finding multiple paths between source and destination node; 2) employing the game theory to obtain the most reliability paths and further optimize shares allocation on these paths; 3) integrating secret sharing scheme, and achieving tradeoff between security risk and delivery ratio according to the tradeoff coefficient. Besides improving fault tolerance, it also improves security. In particular, it makes the eavesdropping attacks maximally difficult as the attackers would have to eavesdrop on all possible paths. Simulation evaluations validate our theoretical results and demonstrate how the routing protocol performs in terms of both security risk and performance.

Heterogeneous collaborative networks have been ever-increasingly concerned due to the constant development of wireless networks. However, before employed in commercial applications, the secure charging should be solved; say, the charging systems with the guarantee of security are vital to support this architecture. Meanwhile, nodes’ non-cooperation behavior should be under control as well. Hence, a secure incentive-based charging solution for heterogeneous collaborative networks integrating cellular and MANET (Ad Hoc) is proposed, utilizing charging receipt to thwart non-reputation attacks. Theoretical verification shows that proposed scheme not only grants the existence of selfish nodes to meet their rational demand, but is robust enough to circumvent various active attacks. Finally, simulation analysis reveals the influence of the parameters in proposed scheme on routing stability and node cooperation in low overhead.