In this article, we propose a planning-operation coordinated mitigation scheme for load redistribution (LR) attacks to overcome the deficiencies of separately designed phase shifting transformer-based mitigation strategies. Specifically, the interactions amongst the defender, attacker, and system are formulated as a trilevel optimization, where the deployment of defense devices and phase shift angles can be optimized according to possible operation state. Based on the proposed load similarity metric, a clustering-based approximate solution is designed to reduce the computational complexity caused by the integration of planning and operation stages. Simulation results on the IEEE 14-bus and 30-bus test systems verify the performance of the proposed mitigation scheme and the clustering-based approximate solution method.

In privacy-preserving mobile network transmission scenarios with heterogeneous client data, personalized federated learning methods that decouple feature extractors and classifiers have demonstrated notable advantages in enhancing learning capability. However, many existing approaches primarily focus on feature space consistency and classification personalization during local training, often neglecting the local adaptability of the extractor and the global generalization of the classifier. This oversight results in insufficient coordination and weak coupling between the components, ultimately degrading the overall model performance. To address this challenge, we propose FedeCouple, a federated learning method that balances global generalization and local adaptability at a fine-grained level. Our approach jointly learns global and local feature representations while employing dynamic knowledge distillation to enhance the generalization of personalized classifiers. We further introduce anchors to refine the feature space; their strict locality and non-transmission inherently preserve privacy and reduce communication overhead. Furthermore, we provide a theoretical analysis proving that FedeCouple converges for nonconvex objectives, with iterates approaching a stationary point as the number of communication rounds increases. Extensive experiments conducted on five image-classification datasets demonstrate that FedeCouple consistently outperforms nine baseline methods in effectiveness, stability, scalability, and security. Notably, in experiments evaluating effectiveness, FedeCouple surpasses the best baseline by a significant margin of 4.3%.

Heterogeneous distributed machine learning systems are vulnerable to Byzantine attacks, where malicious worker nodes disrupt global model convergence by submitting incorrect model updates, leading to degraded system performance. Current aggregation methods that filter based on gradient norm or direction often underperform in heterogeneous environments and struggle to defend effectively against Byzantine attacks. To address this issue, we propose the Direction-Norm Probability Screening (DNPS) algorithm-a novel approach that harmonizes gradient norm and direction to blunt these attacks. DNPS integrates gradient norm and directional information to establish a probability screening mechanism for identifying and filtering Byzantine worker nodes, thereby fortifying the system's defen-sive capabilities and enhancing its robustness against Byzantine attacks. Experimental results show the DNPS algorithm provides more effective protection under both non-attack and Byzantine attack scenarios than existing aggregation methods.

Cross-client data heterogeneity in federated learning induces biases that impede unbiased consensus condensation and the complementary fusion of generalization- and personalization-oriented knowledge. While existing approaches mitigate heterogeneity through model decoupling and representation center loss, they often rely on static and restricted metrics to evaluate local knowledge and adopt global alignment too rigidly, leading to consensus distortion and diminished model adaptability. To address these limitations, we propose FedMate(Code: https://github.com/Dongrun-Li/FedMate.git. Full version of this paper can be found in [39].), a method that implements bilateral optimization: On the server side, we construct a dynamic global prototype, with aggregation weights calibrated by holistic integration of sample size, current parameters, and future prediction; a category-wise classifier is then fine-tuned using this prototype to preserve global consistency. On the client side, we introduce complementary classification fusion to enable merit-based discrimination training and incorporate cost-aware feature transmission to balance model performance and communication efficiency. Experiments on five datasets of varying complexity demonstrate that FedMate outperforms state-of-the-art methods in harmonizing generalization and adaptation. Additionally, semantic segmentation experiments on autonomous driving datasets validate the method’s real-world scalability.

Federated semantic segmentation enables pixel-level classification in images through collaborative learning while maintaining data privacy. However, existing research commonly overlooks the fine-grained class relationships within the semantic space when addressing heterogeneous problems, particularly domain shift. This oversight results in ambiguities between class representation. To overcome this challenge, we propose a novel federated segmentation framework that strikes class consistency, termed FedSaaS. Specifically, we introduce class exemplars as a criterion for both local- and global-level class representations. On the server side, the uploaded class exemplars are leveraged to model class prototypes, which supervise global branch of clients, ensuring alignment with global-level representation. On the client side, we incorporate an adversarial mechanism to harmonize contributions of global and local branches, leading to consistent output. Moreover, multilevel contrastive losses are employed on both sides to enforce consistency between two-level representations in the same semantic space. Extensive experiments on five driving scene segmentation datasets demonstrate that our framework outperforms state-of-the-art methods, significantly improving average segmentation accuracy and effectively addressing the class-consistency representation problem.

Federated learning (FL) is an efficient distributed optimization algorithm but faces significant challenges related to the risk of privacy leakage during training. Many existing FL methods rely on centralized communication network topologies, which have inherent limitations in practical applications. These limitations include vulnerability to single points of failure, susceptibility to communication bottlenecks, and an inability to effectively adapt to dynamic and decentralized environments. To address these challenges, this paper proposes the PODL-RM method, which offers an optimized framework for decentralized learning under time-varying directed communication topologies while ensuring personalized differential privacy (DP) protection for each client. To mitigate the negative effects of data heterogeneity and DP noise perturbation on model performance, PODL-RM combines regularization tuning with model pruning techniques. These components work synergistically to enhance both convergence efficiency and model accuracy. We conduct a rigorous theoretical analysis of the proposed method, formally establishing its convergence in the context of non-convex optimization problems. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art algorithms in time-varying directed communication topologies, yielding superior convergence performance and reduced communication costs.

Personalized federated learning (PFL) is a distributed learning paradigm designed to address data heterogeneity across clients. While PFL enhances model adaptability through local personalization, achieving a balance between robust privacy protection and effective personalized learning remains a critical challenge—particularly for sensitive data. To address this issue, we propose DFed-LaMA, a novel differentially private PFL framework that incorporates adaptive layer-wise model aggregation, optimizing the trade-off between personalization and privacy. Specifically, clients dynamically identify and personalize the most relevant model layers—determined via Kullback-Leibler divergence between local and global models—before applying differential privacy (DP) perturbation and uploading them to the server. Furthermore, we introduce a model-product integration strategy to align local updates with global objectives, mitigating performance degradation induced by DP noise. Extensive experiments on multiple benchmark datasets demonstrate that DFed-LaMA outperforms state-of-the-art methods in classification accuracy, training stability, and privacy guarantees.

Modern power systems benefit from enhanced reliability and efficiency due to advanced information technologies, but face increased cyber vulnerabilities, particularly false data injection attacks (FDIAs) that threaten grid stability. We propose a Transformer-Gated Recurrent Unit (TGRU) framework, integrating Transformer encoders’ feature extraction with GRUs’ temporal modeling. A novel Euclidean distance-based threshold selection method distinguishes legitimate from malicious data, and an FDILocator module accurately identifies attack locations by analyzing discrepancies between TGRU predictions and actual measurements. Experiments on the IEEE 14-bus system validate the approach’s effectiveness and accuracy.

Communication among mobile devices in the Inter-net of Things (IoT) typically relies on fixed security boundaries and centralized trust models, resulting in weak trust mechanisms, unauthorized access, and limited adaptability to dynamic environments. To address this, this paper proposes a zero-trust-based secure communication access control method that follows the principles of zero trust and least privilege, establishing a dynamic trusted framework encompassing identity authentication, real-time trust evaluation, multi-layer access control, continuous behavior analysis, and trajectory visualization. A dynamic trust evaluation model combining static attributes and historical interaction data is designed to ensure that only users who pass trust assessments are granted access. An interaction trust computation method incorporating direct trust, indirect trust, and a time-decay factor is introduced, along with a sliding window algorithm for dynamic threshold updates, enhancing responsiveness to changes in device behavior. A machine-learning-based continuous behavior monitoring mechanism is implemented to perform real-time modeling and detection of performance, traffic, and access patterns, improving anomaly identification and rapid response. The prototype system was validated through multi-device collaborative interaction simulations, demonstrating significant advantages in traffic anomaly detection and communication security over existing approaches.

Cyber-Physical Systems (CPS) are closely connected with human social production and daily life, and ensuring their security is of vital importance. Anomaly detection in CPS has therefore become an important research area for safeguarding their security. However, existing approaches struggle to effectively capture nonlinear spatio-temporal interactions, dynamically model spatio-temporal relationships among variables, and enforce temporal causality, which ultimately result in inaccurate anomaly detection, reduced robustness, and limited applicability in real-world CPS scenarios. To overcome these limitations, we propose FST-AD, a Frequency-Spatio-Temporal Graph Neural Network framework for anomaly detection. FST-AD employs multiscale convolutions with an alternating padding strategy and Fast Fourier Transform (FFT) to jointly extract time-frequency features. The resulting time-frequency features are modeled through an adaptive graph structure learning module to capture evolving spatio-temporal dependencies and complex variable interactions. A message passing neural network (MPNN) combining multi-order graph convolutions and attention mechanisms further enables deep fusion of spatio-temporal features, and leveraging Principal Component Analysis (PCA) driven dimensionality reduction and reconstruction enhances noise suppression and stability in anomaly recognition. Experiments on real-world industrial datasets show that FST-AD achieves significant gains in accuracy, robustness, and generalization; on the SWaT dataset, it surpasses the best baseline by 8.57 and 6.3 percentage points in ROC and PRC, respectively, offering a reliable and scalable solution for CPS anomaly detection.

In load redistribution (LR) attacks, attackers compromise measurement devices at the cyber layer to inject false data, resulting in misoperations in the physical power system. However, most existing methods trace the source of attacks in cy-ber or physical layers separately, where the effective coordination between cyber and physical layers is neither modeled nor utilized. To address this issue, this paper proposes a cross-layer attribution method. Specifically, at the physical layer, a comprehensive evaluation metric is proposed to accurately locate high-risk branches. At the cyber layer, a labeled subgraph isomorphism matching algorithm based on a traceability graph is developed to reconstruct attack paths from log data. To enable cross-layer attribution, a time-topology coupling mechanism is introduced, which can significantly enhance cyber-physical correlation and attribution efficiency. Simulations using a publicly available real-world dataset on the IEEE 39-bus system verify the effectiveness of the proposed method in cross-layer attack attribution.

State estimation methods in smart grids are vulnerable to false data injection attacks (FDIAs). To address this threat, recent research has adopted deep neural networks (DNNs) to detect such attacks. However, DNNs exhibit inherent security flaws, making their decisions susceptible to adversarial perturbations. Exploiting this vulnerability, adversarial FDIAs have been designed to evade DNN-based detection. In this paper, we propose a black-box adversarial false data injection attack leveraging autoencoders. The attack uses an autoencoder to learn the underlying physical model of grid data without prior knowledge of the network topology or detection mechanisms. This is jointly optimized with a surrogate model to generate adversarial perturbations. Experimental results demonstrate that the proposed attack successfully evades both conventional bad data detectors and DNN-based detectors, achieving high success rates in black-box settings. This vulnerability poses a significant security threat to smart grids.

The vulnerabilities of power system state estimation have been widely analyzed recently. However, most of the existing attack model can only pass the bad data detector (BDD) in power system state estimation, where the state-of-the-art neural attack detectors (NADs) are not fully considered. To generate stealthy attack in power system with high penetrated renewable energy sources(RESs), the principle of constructing spatiotemporally stealthy attack is analyzed, where a spatio-temporality principle is proposed to ensure the stealthiness of the attack. A spatiotemporal correlation generation framework is proposed in the improved WGAN-GP framework, which can generate spatiotemporally stealthy false data injection (FDI) attack in power system state estimation deployed with the state-of-the-art NADs. Simulations in the IEEE 14-bus, the IEEE 57-bus and the IEEE 118-bus test systems verify the stealthiness and effectiveness of the proposed spatiotemporally stealthy FDI attack.

Differential privacy is widely employed in decentralized learning to safeguard sensitive data by introducing noise into model updates. However, existing approaches that use fixed-variance noise often degrade model performance and reduce training efficiency. To address these limitations, we propose a novel approach called decentralized learning with adaptive differential privacy via variance-reduced stochastic gradient push (ADP-VRSGP). This method dynamically adjusts both the noise variance and the learning rate using a stepwise-decaying schedule, which accelerates training and enhances final model performance while providing node-level personalized privacy guarantees. To counteract the slowed convergence caused by large-variance noise in early iterations, we introduce a progressive gradient fusion strategy that leverages historical gradients. Furthermore, ADP-VRSGP incorporates decentralized push-sum and aggregation techniques, making it particularly suitable for time-varying communication topologies. Through rigorous theoretical analysis, we demonstrate that ADP-VRSGP achieves robust convergence with an appropriate learning rate, significantly improving training stability and speed. Experimental results validate that our method outperforms existing baselines across multiple scenarios, highlighting its efficacy in addressing the challenges of privacy-preserving decentralized learning.

Federated learning (FL) is an effective mobile edge computing framework that enables multiple participants to collaboratively train intelligent models, without requiring large amounts of data transmission while protecting privacy. However, FL encounters challenges due to non-independent and identically distributed (non-IID) data from different participants. The existing methods, whether focusing on local training or global aggregation, often suffer from insufficient unilateral optimization. Achieving effective local-global collaborative optimization, particularly in the absence of additional reference models or datasets, is both crucial and challenging. To address this, we propose a novel approach:Dual-AggregatedFederated learning based on a tripleSiamese network (FedSiam-DA). This method enhances the FL algorithm on both client and server sides. On the client side, we establish a triple Siamese network incorporating a stop-gradient scheme, which leverages a contrastive learning strategy to control the update directions of local models. On the server side, we introduce a dual aggregation mechanism with dynamic weights for local updates, improving the global model’s ability to assimilate personalized knowledge from local models. Extensive experiments on multiple benchmark datasets demonstrate that FedSiam-DA significantly improves model performance under non-IID data conditions compared to existing methods.

With the widespread application of collaborative learning (CL) technology in mobile-crowdsourcing-related scenarios, special attention should be paid to the privacy disclosure problem therein. Many pioneer noise-perturbation-based methods, particularly the differentially private ones, provide only homogeneous protection, which is insufficient for the heterogeneous protection requirements of many practical CL cases. In this article, we propose a privacy-aware mechanism that uses appropriate Gaussian noises to obfuscate the local and aggregated models. The noise variance is determined based on clients’ different privacy requirements. By zero-concentrated differential privacy, we analyze clients’ privacy-preserving degrees (PPDs) in the uplink and downlink channels. The obtained PPDs demonstrate that the information received by the aggregating server and the peer clients has distinct preservation effects, indicating that our scheme achieves the goal of heterogeneous protection. Moreover, we conduct a theoretical analysis of the performance of the global models aggregated during the iterative process. Finally, we validate the correctness of our theory with experimental results using a real-world data set.

As the Internet of Medical Things booms, the cloud-assisted health monitoring service has attracted extensive attention. The medical institutions often use the Naïve Bayesian classification technology to establish the medical inference models. These models can be outsourced to cloud servers, allowing the remote users without models to utilize well-performing models for medical diagnosis. Existing Naïve Bayesian secure outsourcing schemes mostly use heavy cryptographic primitives or pure additive secret sharing (ASS) technology. In this article, we use searchable encryption technology combined with ASS to design a privacy-preserving Naïve Bayesian classification scheme that can protect the medical institutions' models, the users' medical data, and the final inference result made by cloud servers. Compared with the state-of-the-art, our scheme further reduces the number of communications between the user and the cloud server and reduces the computation complexity of cloud servers from$O(dtf)$to$O(dt)$. We provide the formal security analysis to show that our scheme ensures the necessary security. Through experiments on multiple datasets, we show that our scheme can efficiently handle the classification requests in the test dataset in less than 100 ms.

K-hop Reachability Query (KRQ) is one of fundamental graph queries, which can answer whether a node u can reach a node v within K hops. With the scale of graph data increasing, data owner desires to outsource the local graphs to cloud server. To protect the graph privacy, data owner encrypts graphs before outsourcing them to the cloud server. It imposes a great challenge to KRQ over encrypted graphs. How to realize Privacy-Preserving K-hop Reachability Query (PPKRQ) over encrypted graphs is still an unexplored problem. In this paper, we explore this problem and propose a practical scheme. In order to efficiently answer KRQ over encrypted graphs, we construct the encrypted Breadth-First Spanning Tree table and adjacent list D (BFST-D). Based on encrypted BFST table, we can directly judge whether two query nodes are reachable within K hops when they are in one spanning tree. The encrypted adjacent list D can help answer that two query nodes in different spanning trees. To protect the privacy, we utilize the Paillier cryptographic and Order-Revealing Encryption (ORE) to support the comparison and computation over ciphertexts. As a result, our scheme achieves the sensitive information privacy without losing the ability of querying over encrypted graphs. The security analysis shows that our proposed scheme is secure based on semi-honest cloud server. The extensive experiments show the efficiency of our scheme.

This paper proposes a constraint-aware safety control approach via adaptive dynamic programming (ADP) to address the control optimization issues for discrete-time systems subjected to state constraints. First, the constrained control framework is established via the primal-dual approach with the modified Lagrangian based on the relaxed barrier function. Herein, the Lagrangian multiplier is designed to achieve the trade-off between optimization performance and state constraints. In addition, the sub optimality of the dual method is built by proving that the dual gap can be arbitrarily small. And the value iteration algorithm is utilized to implement the constraint-aware ADP controller. Furthermore, the weight estimation error is proved to be bounded when the learning rate satisfies a given sufficient condition. Finally, numerical simulation proves the effectiveness and superiority of the proposed method.

Public auditing is a significant technique in cloud-based Internet of Things (IoT) systems, which enables the verifier to check the integrity of IoT data stored in the cloud. Nowadays, data become a core property for owners. Once the data of one owner are sold to another one, the ownership of these data has to be transferred. However, the existing public auditing schemes with data transfer require all the authenticators corresponding to the transferred data to be transformed to the new ones for integrity auditing. It incurs significant computation cost because of recomputing the new authenticators for all transferred data, especially when a vast quantity of data is being transferred. In addition, the data privacy and the identity privacy of the data owner cannot be protected for the verifier in such schemes. Thus, how to achieve efficient data transfer and privacy protection are key challenges in public auditing with data transfer for cloud-based IoT data. In this article, we propose a privacy-preserving identity-based public auditing scheme with efficient data transfer for cloud-based IoT data (PPADT). In PPADT, all the authenticators corresponding to the transferred data blocks do not need to be transformed. We only need to transform an aggregated authenticator in the integrity auditing phase. It means that the computation cost of data transfer is independent of the number of transferred data blocks. Furthermore, the data owner’s identity privacy can be ensured with the assistance of the private key generator. The data privacy can also be guaranteed by employing the random masking technique.

Distributed classification learning (DCL) is a promising solution to establish Internet of Things-based smart applications, especially due to its strong ability in dealing with large-scale and high-concurrency data. However, the performance of DCL may be seriously affected by the label flipping attack (LFA). Regarding the LFA-resilient learning problem, most existing works are built in more centralized settings. The work addressing the secure DCL issue makes an assumption that the label flipping rates are symmetric and available for scheme design. In this article, we remove this assumption and propose an LFA-resilient DCL scheme, named FENDER, without knowing the asymmetric flipping rates. The challenge is to guarantee both attack resilience and algorithm convergence. We carefully integrate a resilient loss and the alternating direction method of the multiplier scheme, making FENDER resilient to LFA. Further, we systematically analyze the performance of FENDER according to a metric reflecting the models obtained by all the servers at different iterations. In addition, we discuss and compare FENDER with some existing methods from the aspects of algorithm establishment and performance guarantee. Finally, extensive experiments with multiple real-world data sets are performed to validate the developed theory and evaluate the performance of the trained models.

The key exposure is a serious threat for the security of data integrity auditing. Once the user's private key for auditing is exposed, most of the existing data integrity auditing schemes would inevitably become unable to work. To deal with this problem, we construct a novel and efficient identity-based data integrity auditing scheme with key-exposure resilience for cloud storage. This is achieved by designing a novel key update technique, which is fully compatible with BLS signature used in identity-based data integrity auditing. In our design, the Third Party Auditor (TPA) is responsible for generating update information. The user can update his private key based on the private key in one previous time period and the update information from the TPA. Furthermore, the proposed scheme supports real lazy update, which greatly improves the efficiency and the feasibility of key update. Meanwhile, the proposed scheme relies on identity-based cryptography, which makes certificate management easy. The security proof and the performance analysis demonstrate that the proposed scheme achieves desirable security and efficiency.

With the rapid development of Internet of Things (IoT), edge computing has been widely applied as a novel computing paradigm. Securely outsourcing intensive tasks to edge servers is becoming increasingly pervasive. It is a nice approach for resource-limited IoT devices to accomplish heavy computing tasks. Matrix inversion is a basic but time-consuming operation, which has a wide range of applications in IoT. The current privacy-preserving outsourcing schemes for matrix inversion cannot support parallel computing based on multiple edge servers. As a result, they cannot well satisfy the requirement of fast response for computation in IoT. In order to deal with this problem, we propose two privacy-preserving parallel outsourcing schemes for matrix inversion in IoT. In the first scheme, we design a novel method to generate a random matrix, which is used to blind the inputted original matrix. In this scheme, two edge servers compute the inversion of the encrypted matrix in parallel to improve the computational efficiency. To further improve the efficiency, we design a novel subtasks partitioning and assignment strategy and propose the second scheme by balancing the computing load of edge servers. We analyze the correctness, security, and verifiability of the proposed schemes. And we provide theoretical analysis and experimental results to demonstrate the performance advantages of the proposed schemes.

With the development of cloud computing and the advent of Internet of Things(IoT), outsourcing computation, as an important application of cloud computing, has been widely researched in the field of academic and industry. The convex optimization problem, as a most common mathematical problem, often appears in some machine learning algorithms and smart grid designs. However, the process of solving the convex optimization problem is very complicated and time-consuming. For some resource-constrained IoT devices, there are no enough computation resources and storage resources to deal with this problem. In this paper, we proposed an efficient and secure outsourcing algorithm for solving the large-scale convex optimization problem with equality constraints in IoT. Our proposed algorithm can not only reduce the computational complexity on the client side, but also protect the client’s sensitive data from being disclosed to the dishonest cloud server. In addition, the client can detect the malicious behavior from the cloud server with probability approximately 1. Finally, we give a theoretical analysis about correctness and security, and conduct experiments to show the feasibility of our proposed algorithm.

Object recognition of hyperspectral remote sensing images based on machine learning is widely applied in many industries. However, the efficiency of the training and recognizing process of object recognition on hyperspectral remote sensing images is a critical issue since it involves complex matrix operations and large scale training data sets, especially for resource-constrained devices. One solution is to outsource the heavy workload of object recognition on hyperspectral remote sensing images to a cloud server. Nonetheless, it may bring some security problems when the cloud server is untrustworthy. Therefore, how to enable resource-constrained devices to securely and efficiently accomplish the training and recognizing process of object recognition on hyperspectral remote sensing images is of significant importance. In this article, we propose a secure and efficient scheme to outsource the object recognition on hyperspectral remote sensing images to the untrustworthy cloud server. The proposed scheme can protect the privacy of the computation input and output. Also, we develop an effective verification approach in our scheme that can detect the misbehavior of cloud server with the optimal probability 1. The theoretical analysis and experimental results indicate that our proposed scheme is secure and efficient.

With the development of Internet of Things (IoT) and 5G, edge computing, as a new computing paradigm, has been widely popularized in academia and industry. Due to the distributed architecture and being close to the user, edge computing can faster respond to the IoT device's request and provide a better quality of service for IoT applications. An important application of edge computing is to outsource the complicated computation task to the nearby edge nodes. Modular exponentiation is widely considered as one of the most common and expensive operations in cryptographic protocols. As far as we know, all secure outsourcing algorithms of modular exponentiation are based on the centralized cloud server, but not based on multiple edge nodes. In this article, we propose the first secure and distributed outsourcing algorithm for modular exponentiation (fixed base and variable exponent) under the multiple noncolluding edge node model. In our algorithm, the exponent is divided into a certain number of parts. In addition, we propose another secure and distributed outsourcing algorithm of modular exponentiation (variable base and variable exponent). The user can protect the privacy in the process of outsourcing and detect the invalid results from edge nodes with high probability. Finally, we provide the experimental evaluation to support that our proposed algorithms are efficient on both the user side and the edge node side.