As a distributed machine learning paradigm, federated learning enables collaborative training among multiple clients while preserving data privacy. However, in practical applications, it faces the challenge of domain shift caused by data heterogeneity, which limits the generalization performance of the global model on unseen target domains. To address this issue, this paper proposes an image style transfer-empowered federated domain generalization method. Specifically, the method first enriches the domain diversity of local data through image style transfer techniques. Meanwhile, we introduce a predictive consistency regularization term into the optimization objective, it ensures the model maintains stable outputs when processing both original samples and their restyled versions, thereby mitigating the overfitting to local data domains and facilitating the learning of domain-invariant features. Furthermore, a generalization capability-aware aggregation weight optimization strategy is developed. By leveraging an unlabeled public dataset on the server side and its restyled versions to simulate unseen target domains, the strategy evaluates the generalization performance of client models and dynamically adjusts aggregation weights accordingly, which enhances the contribution of clients with higher generalization capabilities to the global model. Finally, experiments validate the effectiveness of both the local regularization and aggregation weight optimization strategies. On the PACS and Office-Home datasets, the proposed method achieves higher average test accuracy compared to baseline methods, along with faster convergence speed. Moreover, we additionally conduct experiments on the Camelyon17 tumor classification dataset, which further verify the robustness and practical applicability of the proposed method in real-world scenarios.

Federated learning (FL), as a representative distributed learning paradigm, has achieved remarkable success. However, most existing FL studies assume that each client holds correctly labeled data, whereas in reality, noisy labels are ubiquitous on the client side. To mitigate the adverse impact of noisy labels on FL performance, we propose a robust FL method with double denoising. Specifically, we first perform primary denoising based on the idea of cross-prediction, where two global models are trained and mutually used on each client to identify and filter out mislabeled samples. Next, after fine-tuning the models, we develop secondary denoising by detecting and removing residual noisy samples through clustering based on loss values. In addition, we design a noise-tolerant local training strategy that dynamically assesses the influence of noisy data on local updates and applies differentiated update rules to prevent overfitting. Finally, experimental results on three benchmark datasets, including the real-world noisy dataset Clothing1M, demonstrate that our method effectively removes label noise, delivering improved performance of the global model.

Estimating the 6-DoF posture of parts in assembly-based modeling is a critical task in the fields of computer graphics, computer vision and robotics. A typical scenario involves enabling a machine agent to automatically assemble IKEA furniture using the provided parts. This paper presents HiFormer, a novel Hierarchical Transformer with Box-packed Positional Encoding, designed for highly automatic 3D part assembly. Our method addresses three important issues commonly encountered in 3D part assembly: 1) How to mitigate the overfitting problem associated with Transformer-based feature learning for 3D point clouds? 2) How to effectively model the relationships between the intragroup and intergroup parts? 3) How to compute positional encoding and integrate it into the Transformer for parts with diverse geometric forms in the coarse-to-fine assembly process? These challenges are tackled through three key contributions: 1) a multi-task 3D Swin Transformer with a two-stage training strategy for feature extraction, 2) a novel hierarchical Transformer for capturing part relationships at flattening, intragroup, and intergroup levels, and 3) an innovative box-packed positional encoding that enhances the Transformer by incorporating query, key, and value information derived from relative box positions. On the PartNet benchmark, our method outperforms the state-of-the-art PWH-MP model on three representative categories-Chair, Table, and Lamp-, achieving average improvements of 2.84% in Part Accuracy (PA) and 3.72% in Connection Accuracy (CA) for diversity modeling (with noise), and 3.55% in PA and 3.21% in CA for deterministic modeling (without noise).

As a distributed machine learning approach, federated learning (FL) overcomes the challenge of data silos. However, FL cannot fully ensure the assumption that all federated entities are honest and trustworthy, and evolving inference attack techniques pose serious threats to the privacy of FL. To address these challenges, this paper proposes a conditional generative adversarial network (CGAN)-based high-performance privacy-enhanced FL algorithm. This algorithm aims to improve the performance of FL while addressing the issue of ineffective partial data desensitization and privacy protection. Specifically, it extends the auxiliary training data by designing a CGAN-based auxiliary data generation mechanism and solves the problem of overfitting. To enhance privacy protection, it adopts the concept of dual-model coexistence and designs an improved deep mutual learning (IDML) model inter-training method. This enhances the effectiveness of knowledge distillation between models. It also ensures that sensitive information is only stored locally on the client. Additionally, a high-performance model aggregation method based on predictive distribution is proposed to further improve the performance of FL. Finally, simulation results demonstrate that the proposed algorithm not only performs well in maintaining the performance of FL but also can resist various inference attacks.

Traditional respiratory monitoring methods typically use wearable sensors. In recent years, Radio Frequency Identification (RFID) tags have emerged as potential respiratory monitoring devices. However, existing research only detects respiration under static or simple body movements such as forward and backward motions, which limits the availability of RFID. In this article, an RFID-based respiratory monitoring system under body motion disturbances is proposed. In order to eliminate the effect of body movement on the signal, reference tags are placed on the user’s shoulders. The phase between different tags is calibrated and aligned to remove the discreteness. Then, a sliding window peak-seeking algorithm is designed to calculate respiratory rate over time. The system measures respiratory rate during daily exercise and maintains an accuracy of no less than 86.12%, even when influenced by lying down and turning. Additionally, the system can analyze estimated respiratory rates and intervals to provide alert information.

As a privacy-preserved distributed machine learning paradigm, federated edge learning (FEL) was designed to absorb knowledge from user devices to construct intelligent services without transmitting raw data. However, this paradigm depends on the local training and model parameter transmission of user devices, therefore the computing power, storage capacity and network resources of the devices become the key factors to achieve energy well-budgeted and timely message transmission FEL. While in the wireless networks, those resources for devices are normally heterogeneous or limited. This paper aims to offer tangible solutions for optimal convergence and Quality of Service (QoS) assurance of FEL in wireless networks. First, we define a mathematical model for energy-efficient message transmission of FEL and formulate an optimization problem involving device sampling and resource allocation to attain optimal training convergence within energy and time constraints. Second, we theoretically analyze the impact of limited resources on sampling strategies and training convergence, thus simplifying the optimization problem for solvability. Third, we introduce an iterative heuristic algorithm that utilizes available resources to reduce client sampling bias. Extensive experiments show that our method can effectively obtain the debiased sampling strategy, and outperforms similar methods by minimizing device disconnection due to energy use and enhancing model convergence and performance.

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.

As an extension of machine unlearning in distributed scenarios, federated unlearning gains significant attention. However, federated unlearning remains challenging, as many studies require additional resources, such as auxiliary dataset or storage, to achieve high-quality models. These requirements incur extra costs and are often difficult to satisfy in practical applications. To address these issues, we propose a flexible client-level federated unlearning algorithm with prototypes, called FedUP. Specifically, our algorithm consists of two components: prototype-based unlearning and model recovering. First, we design a prototype-based unlearning strategy that uses prototypes of the erased client to guide the unlearning process, and maximizes the prototype loss between the remaining and erased clients to unlearn the information. It does not rely on historical storage updates or additional standard datasets, making the unlearning process more streamlined. To mitigate performance degradation from the unlearning process, we develop a brief model recovering approach guided by global prototypes to swiftly and efficiently restore models' accuracy on the remaining datasets. Unlike other unlearning algorithms, our approach exchanges prototypes instead of model parameters, significantly reducing communication overhead. Finally, we empirically evaluate the proposed algorithm from multiple perspectives on two datasets, demonstrating that our algorithm can achieve high-quality unlearned models with minimal communication cost.

Federated learning (FL) is a popular distributed learning framework that is proposed to address privacy concerns in traditional machine learning. However, recent research has highlighted an issue where model or gradient updates can be exploited to infer sensitive information from the training data, resulting in severe privacy leakage. Existing defenses against gradient leakage attacks often suffer from high computation overhead or compromised model performance. In addition, most defense methods lack sufficient protection for labels. To overcome the above shortcomings, we develop a compressed sensing (CS)-based practical and efficient privacy-preserving FL scheme. In order to provide simultaneous protection for both original data and labels, we propose a CS-based gradient perturbation method, which eliminates the information in the gradient that is commonly exploited by attackers to extract labels, and increases the discrepancy between the perturbed and original gradients. Meanwhile, double aggregation is adopted together to ensure individual gradients are not easily disclosed by attackers. We also design a novel gradient reconstruction method that adaptively estimates the true gradient sparsity used for decompressing, thereby improving the model performance in practical scenarios. Furthermore, our CS-based gradient compression reduces communication overhead and requires low computation overhead as it only involves fast matrix multiplication. Extensive experiment results demonstrate the strong privacy protection effects of our proposed scheme compared to other approaches across various settings, with advantages in terms of communication overhead, computation overhead, and model accuracy.

Federated learning (FL) has been widely adopted for intelligent skin lesion diagnosis due to enabling collaborative model training across distributed data sources while preserving data privacy. However, traditional centralized learning paradigm, which collects data from distributed institutions to train neural models centrally, has the risk of privacy leakage. FL enables collaborative learning on training an artificial intelligence (AI) model among scattered medical institutions (MIs) without directly sharing raw skin lesion data, which is a promising solution. However, FL generally suffers from performance deterioration due to heterogeneous skin lesion data sets, and the differences in computing power and communication conditions among different MIs can impact the training efficiency. In this article, we present a prototypes contrastive learning (PCL) empowered intelligent diagnosis mechanism for skin lesion. Our mechanism designs a PCL-based local training to overcome data heterogeneity by designing two special losses, where prototypes-based contrastive loss can increase the interclass variance and reduce the intraclass variance of various skin lesions, and prototypes-based consistent regularization loss can prevent the shift of local updating. Meanwhile, a hierarchical FL aggregation mechanism is proposed, where asynchronous and synchronous aggregations are combined to ensure the training efficiency in case of large differences in computing power and communication conditions. Finally, simulation results show that our developed method can effectively mitigate the adverse impact of heterogeneity skin lesion data sets and provide efficient FL training.

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.

In practical applications, federated learning (FL) suffers from slow convergence rate and inferior performance resulting from the statistical heterogeneity of distributed data. Personalized FL (pFL) has been proposed to overcome this problem. However, existing pFL approaches mainly focus on measuring differences between entire model dimensions across clients, ignore the layer-wise differences in convolutional neural networks (CNNs), which may lead to inaccurate personalization. Additionally, two potential threats in FL are that malicious clients may attempt to poison the entire federation by tampering with local labels, and the model information uploaded by clients makes them vulnerable to inference attacks. To tackle these issues, (1) we propose a novel pFL approach in which clients minimize local classification errors and align the local and global prototypes for data from the class that is shared with other clients. This method adopts layer-wise collaborative training to achieve more granular personalization and converts local prototypes to the frequency domain to prevent source data leakage; (2) To prevent the FL model from misclassifying certain test samples as expected by poisoners, we design a robust aggregation method to ensure that benign clients who provide trustworthy model predictions for its local data are weighted far more heavily in the aggregation process than malicious clients. Experiments show that our scheme, especially in the data heterogeneity situation, can produce robust performance and more stable convergence while preserving privacy.

Federated learning (FL) is an emerging distributed learning framework that can reduce privacy leakage risks by not explicitly sharing private data, but FL still has privacy leakage issues. Recently, many FL security schemes were proposed to improve the protection capabilities of FL, but they cannot realize a desirable tradeoff between privacy and model performance. In this work, we propose a dynamic privacy-enhanced federated learning algorithm based on gradients perturbation to solve above issue. First, instead of directly using original feature of training data, we generate new feature of training data by maximizing the distance between training data and resulting data that inverse mapping by new feature, and minimizing the distance between original and new feature. Next, the dynamic mixed gradients are obtained by combining gradients generated by original and new feature with variable weights. The dynamic mixed gradients-based model training can effectively prevent the original training data leakage and adjust privacy preservation strength adaptively. Furthermore, to make iDLG difficult to infer labels of training data, we perturb mixed gradients by transforming part of positive gradients into negative gradients. Finally, the experiments demonstrate that the algorithm proposed in this paper can effectively resist iDLG attack, and it has significant advantages in protection effect compared with other security schemes.

Satellite federated learning (FL) applies distributed training framework to space, which enables collaborative learning on training an artificial intelligence model among scattered ground devices without directly sharing raw data. In low earth orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging. However, recent researches have stated that heterogeneous data of different ground devices may lead to the shift of local training, and this degrades the performance of FL model. Several solutions are developed to overcome this challenge. However, the ability of these solutions to solve data heterogeneity problem is limited, and their generalization performance is poor. Under this background, we propose a satellite-terrestrial collaborative federated learning with alternating contrastive training (FedAC). Firstly, we design a model initialization algorithm to make initial model learn more prior knowledge, which can increase the generalization performance of FL model and better adapt to new label domain. Meanwhile, an alternating contrastive training algorithm is proposed, where only local public model is uploaded to the satellite station for aggregation and local private model is saved locally, it decreases the impact of data heterogeneity. In addition, two special contrastive losses are introduced to correct local training direction, which can prevent the shift of local updating. Finally, simulation results show that our developed method can effectively mitigate the adverse impact of data heterogeneity and perform better when the ground device with new label domains.

In recent years, federated learning (FL) has gained increasing attention as a promising approach for preserving privacy in machine learning, as it enables multiple clients to jointly train a shared model while avoiding the need to share their raw data. However, many existing works towards FL assume that the data owned by clients are correctly labeled, which is unrealistic. To mitigate the performance degradation incurred by incorrect labels (i.e., noisy labels), we propose a robust federated learning method with parameter classification and weighted aggregation. Specifically, it classifies the parameters of the deep neural network into critical and noncritical ones according to whether they are important to fit data with clean labels, and updates these two kinds of parameters based on different rules to prevent the model from overfitting noisy labels. Furthermore, a weighted aggregation strategy is designed for the global training phase, which enhances the predictive performance of the global model by strengthening the contributions of clients with higher learning efficiency. Finally, the experimental results demonstrate that the proposed method efficiently addresses the performance degradation caused by noisy labels with low latency and exhibits superior stability compared to the baselines.

Recently, federated learning has been widely applied, but the performance of the local model will be greatly decreased when the data distribution across different clients is heterogeneous. In this paper, we propose a decentralized two-stage federated learning scheme with knowledge transfer. Specifically, a ring federated learning model with knowledge transfer is constructed, by which users can acquire accurate service results. Meanwhile, a knowledge accumulation method based on conditional generative adversarial networks (CGAN) is designed, which can accumulate information from the previous edge nodes without compromising privacy. Furthermore, the accumulated global knowledge is learned by edge nodes through knowledge distillation to improve the performance of models on the global test dataset. Finally, the simulation results demonstrate that our proposed scheme achieves better classification accuracy and stability compared with the state-of-the-art methods.

Convolutional Neural Networks (CNNs), a widely recognized deep learning algorithm, have been utilized in various domains such as smart cities and healthcare. However, the remarkable performance of CNNs is accompanied by high resource overhead and deployment complexity. To address these challenges, CNN compilers have been developed to simplify convolutional operations for edge device deployment. One of the crucial components in CNN models is the General Matrix Multiply (GEMM) operation, which serves as the main computational kernel. In previous studies, efforts were made to improve the computation speed of GEMM by modifying the matrix calculation sequence, but they did not fully exploit the computing resources of edge devices. In this paper, we propose a novel GEMM-based acceleration algorithm, named microGEMM. The microGEMM algorithm divides convolutional data to reduce the memory access times during the GEMM calculation process. Moreover, the algorithm employs instruction-level optimization in the GEMM calculation unit, decreasing the cache miss rate. To better evaluate the superiority of microGEMM on resource-constrained devices, two edge-oriented metrics are proposed, namely CCPS & CCPoE. The microGEMM algorithm is implemented in C++ and compared with the standard GEMM algorithm (naiveGEMM) and the GEMM of the open-source Basic Linear Algebra Subprograms (BLAS) library (openblasGEMM). The experimental results demonstrate that microGEMM achieves a significant speedup, ranging from 5.67 × to 14.19 ×, compared to naiveGEMM.

In recent years, federated learning is the most commonly used framework for collaborative training under the protection of privacy, and has been successfully applied to smart healthcare. The distribution of data in the federated network is usually non-independent and identically distributed (non-IID) and imbalanced, which worsens the performance and increases the gap between the local and global models. Additionally, the similarity of the medical images makes it challenging to identify. In this paper, we propose a dual aggregated federated learning with depthwise separable convolution for smart healthcare. Specifically, a diagnostic network based on depthwise separable convolution is designed, and the residual connection is introduced, which can make full use of the feature information in the medical image and improve the accuracy of disease diagnosis. Meanwhile, we design a dual federated aggregation algorithm to reduce the impact of parameter differences on multi-client model federated aggregation and improve the performance of the global model. Finally, the experimental results illustrate that our proposed algorithm achieves significant performance advantages compared with other existing methods.

Based on the vulnerability of federated learning (FL) to inference attacks and the high computation overhead, lack of label protection and degraded model performance occurred in existing defense methods, we design an efficient privacy-preserving federated learning scheme based on compressed sensing (CS), where CS is used as both a compression method and an encryption method. Double aggregation is adopted together to ensure that gradients are not generally disclosed in a way that would allow attackers to infer private information. Meanwhile, gradient perturbation is implemented through CS-based decompression algorithm, and it also zeros the gradients for the fully connected layer which is the most important in label restoration. The proposed scheme can provide image protection and label protection simultaneously, while few additional computing resources are required, making it appropriate for IoT scenarios. Simulation results demonstrate our scheme’s effective and efficient defense under different settings with negligible impact on the model performance.

Federated learning (FL) as an emerging edge intelligence paradigm allows clients to jointly train a model without exchanging raw data. Due to its excellent performance in privacy protection, FL has practical application in many areas. However, data heterogeneity across clients is a prevalent phenomenon and poses a significant challenge to FL. Although many FL algorithms have been proposed to address the issue of performance deterioration under non-independent and identically distributed (Non-IID), the improvement in performance is not significant. In this paper, we propose knowledge discrepancy-aware federated learning (KDAFL). It evaluates the local model with regard to each class and overall learning effect based on the discrepancy between local and global knowledge. In this way, each client assigns a new weight to each category of cross-entropy and decides if knowledge distillation is to be conducted. The awareness of discrepancy allows the client to adjust the local training according to the characteristics of the knowledge it learned, thus better solving the Non-IID issue. Extensive experiments have demonstrated the effectiveness of KDAFL, particularly in terms of the improvement of global model accuracy compared to other state-of-the-art algorithms.

Traditional solutions for human action recognition usually rely on sensor or video methods. However, these methods have some limitations, such as inconvenient portability, light intensity influence, privacy protection, etc. In this article, an RFID-based nonwearable human action recognition scheme is proposed. In order to reduce the occlusion effect of the human body on the signal and increase the diversity of the reflected signal, a tags array is constructed. The data of phase and RSSI are fused as feature data to enhance the diversity of data. Furthermore, a combined processing method is proposed to eliminate thermal noise generated by the equipment and reduce the interference caused by the environment. Then, an action segmentation algorithm is designed to align the RF signals of human action. Finally, an efficient human action signal classification model is constructed using the spatiotemporal graph convolutional neural network (STGCN). Extensive experiments demonstrate that the overall accuracy rate of the system for human action recognition is 92.8%. Compared with the comparative mainstream recognition algorithms, STGCN shows better classification performance in terms of identification precision. In addition, multimodal RFID data fusion also improves the accuracy of identification.

In recent years, automatic skin lesion diagnosis methods based on artificial intelligence have achieved great success. However, the lack of labeled data, visual similarity between skin diseases, and restriction on private data sharing remain the major challenges in skin lesion diagnosis. In this article, first, we propose a federated contrastive learning framework to break down data silos and enhance the generalizability of diagnostic model to unseen data. Subsequently, by combining data features from different participated nodes, the proposed framework can improve the performance of contrastive training. To extract discriminative features during on-device training, we propose a contrastive learning based intelligent skin lesion diagnosis scheme in edge computing networks. Specifically, a contrastive learning based dual encoder network is designed to overcome training sample scarcity by fully leveraging unlabeled samples for performance improvement. Meanwhile, we devise a maximum mean discrepancy based supervised contrastive loss function, which can efficiently explore complex intra-class and inter-class variances of samples. Finally, the diagnosis simulations demonstrate that compared with existing methods, our proposed scheme can achieve superior accuracy in both on-device training and distributed training scenarios.

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of patients, which may limit the diagnosis accuracy fundamentally. In this paper, we propose a metadata and image features co-aware personalized federated learning scheme for smart healthcare. Specifically, we construct an intelligent diagnosis model, by which users can obtain fast and accurate diagnosis services. Meanwhile, a personalized federated learning scheme is designed to utilize the knowledge learned from other edge nodes with larger contributions and customize high-quality personalized classification models for each edge node. Subsequently, a Naïve Bayes classifier is devised for classifying patient metadata. And then the image and metadata diagnosis results are jointly aggregated by different weights to improve the accuracy of intelligent diagnosis. Finally, the simulation results illustrate that, compared with the existing methods, our proposed algorithm achieves better classification accuracy, reaching about 97.16% on PAD-UFES-20 dataset.

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

Federated Learning (FL) is a well discussed distributed training framework, which allows scattered clients to collaboratively train a central model without directly sharing raw data. However, recent researches have stated that the model updates or gradients uploaded by FL can be used to infer sensitive data of clients, and this attack poses severe threats to FL. Several solutions are developed to address this threat. Although these solutions can achieve privacy preservation to a certain extent, their accuracy is severely degraded, and they are unable to provide strong privacy protection. Under this background, we propose a double perturbation-based privacy-preserving federated learning method, in which a feature extractor and an additional blurry function are utilized to improve the objective function of Conditional Generative Adversarial Networks (CGANs) and the generated data by CGANs are mixed with real data to construct fake-training data. Meanwhile, we design an algorithm to perturb the information contained in the gradient of fully connected layers that is most favorable for the attacker to reconstruct data. Finally, simulation results show that our developed method can effectively resist inference attack with a negllgible decline in accuracy.

In recent years, automatic skin lesion diagnosis methods based on artificial intelligence (AI) have achieved great success. However, the lack of rich available training data and visual similarity of various skin diseases remain the major challenges in intelligent skin lesion diagnosis. In this paper, we propose a contrastive learning based intelligent skin lesion diagnosis (CL-ISLD) scheme in edge computing networks. Specifically, an edge computing based intelligent skin lesion diagnosis network is constructed, which can provide the convenient and quick online diagnosis service to users nearby. Meanwhile, a contrastive learning based dual encoder network is designed to overcome training sample scarcity by fully leveraging unlabeled samples for performance promotion. Subsequently, we devise a maximum mean discrepancy (MMD) based supervised contrastive loss function, it can efficiently explore complex intra-class and inter-class variances of samples. Finally, the simulation results demonstrate that the proposed CL-ISLD obtains competitive diagnosis accuracy compared with existing representative works and achieves relatively more balanced performance among classes in inadequate and imbalanced dataset.

Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

Although current proposed compression schemes achieve better performance than traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data, and the design of the projection (measurement) matrix cannot satisfy the requirement of real scenarios adaptively. Hence, well-designed clustering algorithm is needed to further explore strong spatial correlation, and an adaptive measurement matrix is also needed to ensure exact data recovery. In this paper, we propose a fog-based optimized Kronecker-supported compression scheme to address the above shortcomings and achieve better compression results in the industrial Internet of Things (IIoT). Our scheme first leverages a k-means-based clustering algorithm that explores the spatial correlation among sensory data, which can obtain better compression effects with less communication overhead. It then develops a novel Kronecker-supported two-dimensional data compression mechanism at the fog node, which can ensure the recovery of the original data from the compressed data with high precision; this mechanism can also reduce the communication overhead between fog and cloud nodes significantly. Next, a Kronecker concatenated measurement matrix optimization problem is formulated for meeting the requirement of real scenarios adaptively, and an efficient solution algorithm is developed to obtain the optimal value and ensure that the stringent precision requirements of industrial applications are satisfied. Finally, simulation results show that our proposed scheme is energy efficient and can achieve better clustering results and recovery performance for sensory data, for example, the energy consumption is reduced by 6.8 percent after clustering operation, and the relative reconstruction error of temperature data is improved by an average of 15.8 percent with the same energy saving effect.

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

Fog computing emerges as a promising mode to meet the stringent requirement of low latency in industrial Internet of Things (IIoT). By offloading partial computation-intensive tasks from fog node to cloud server, the computation experience of users can be further improved in fog computing system. In this paper, we develop an energy-efficient computation offloading scheme for IIoT in fog computing scenario. The purpose is to minimize energy consumption when computation tasks are accomplished within a desired energy overhead and delay. It has a comprehensive consideration on the components of energy consumption at fog node, which includes the energy consumption of local computing, transmitting and waiting states. To address this energy minimization problem, an accelerated gradient algorithm is proposed, it can find the optimal offloading ratio with a fast speed that improves the convergence speed of traditional method. Finally, the numerical results reveal that the proposed offloading scheme is superior to the local computing and full offloading schemes in terms of energy consumption and completion time, and further confirm the advantage of convergence rate.

The D2D (Device-to-Device) communication has been very popular as it is a promising and low-cost solution to reduce the burden on the cellular network. However, there are rare concerns about the distribution and pairing of DUEs(D2D user equipments), which have a significant impact on QoS (Quality of Service) of D2D communication. In this paper, we propose a novel algorithm based on the coalitional game to optimally adjust the distribution of DUEs. The proposed algorithm aims to form the optimal coalition structure, which achieves a balance between the throughput and power consumption of each coalition, obtaining the enhanced QoS of D2D. We show that our algorithm is superior to the benchmark models in terms of the throughput and energy efficiency of the DUE coalition. To further improve the QoS, we also propose a method to predict and maximize the pairing probability of DUEs. The proposed prediction method adopts the Logistic Regression to model the global pairing probability according to the communication parameters of DUEs. Experimental results show that the proposed prediction method is significantly superior to the benchmark methods in terms of prediction accuracy. In addition, the pairing probability maximization algorithm proposed also significantly improves the pairing probability.

In data-rich everything connected world, the rapid and green data processing is essential, especially for some delay-sensitive and computation-intensive tasks. Motivated by these requirements, an energy and delay co-aware fog computation offloading mechanism is conceived in this paper. Specifically, we formulate a weighted sum minimization problem of task completion time and energy consumption at the local fog for achieving efficient task computation. Further, a deep learning-based joint offloading decision and resource allocation (DL-JODRA) algorithm is developed to address such problem by jointly optimizing offloading action, local CPU, bandwidth and external CPU occupation ratios. The optimal offloading decision based comprehensive optimization consideration of network resources further improves the network efficiency. Finally, the extensive simulation results demonstrate that the proposed DL-JODRA can achieve optimal offloading decision with low computation resource requirement and gain significant reduction on network costs (i.e., delay and energy) comparing with benchmark methods.

The property of performing data processing near the source of the data (i.e., at the edge of the network) makes the fog computing more suitable for networking environment of big data. For the sake of achieving efficient big sensory data collection with privacy preservation, this paper proposes a fog computing assisted efficient privacy preserving data collection scheme for big sensory data. In the proposed scheme, the designed layer-aware fog computing architecture provides effective support for exploring the spatio-temporal correlations and avoids long-distance communication with cloud center for utilizing the computation capabilities of local devices. Meanwhile, the proposed sampling perturbation encryption method protects the data privacy against eavesdropper and active attackers without sacrificing the data correlation, and it facilitates the simultaneous executing of decrypting and decompressing operations for encrypted sampling data. Furthermore, the developed data processing at fog node reduces the amount of redundant data transmission significantly, and the formulated optimization model for measurement matrix ensures the high precision of data reconstruction. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection scheme with strong privacy preservation property.

According to efficient performance requirement of big sensory data compression and collection, this paper proposes a fog computing based optimized compressive data collection scheme to enhance recovery quality of original data. In this scheme, mutual correlations of big sensory data are exploited fully owing to the designed data collection architecture. The data processing of fog node urges the computation capability of edge device can be utilized effectively, and which reduces the amount of data transmission significantly. At the same time the constructed encoding and decoding methods among sensory, fog and cloud nodes guarantee successful performing of conventional compressed sensing (CS) reconstruction algorithm with overwhelming probability. In addition, since recovery error is proportional to the mutual coherence among measurement matrix, network coding (NC) transformation matrix and sparsifying basis, a measurement matrix optimization algorithm is constructed to minimize the mutual coherence for stabilizing and enhancing data recovery quality. The desired solution of mutual coherence can be achieved by integrating the alternating minimization and low-pass filtering methods. Simulation results illustrate that the constructed optimization algorithm can obtain the optimal value of mutual coherence, and reconstruction quality of our developed scheme is higher as compared with other compressive data collection schemes.

Although current proposed compression schemes achieve a better performance compared with traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data. Well-designed clustering algorithms are needed to explore strong spatial correlation. In this paper, we propose a k-means based Kronecker supported two-dimensional (spatio-temporal) compression scheme to achieve better compression results. Our scheme first leverages a k-means based clustering algorithm that explores the spatial correlation among sensory data. Then it develops a novel two-dimensional data compression mechanism, which can recover the original data from the compressed data with a high precision. Simulation results show that our proposed scheme is energy-efficient and can achieve better clustering results and recovery performance compared with other schemes for sensory data.

Although the existing compressed sensing (CS) based spatio-temporal data compression schemes can significantly decrease communication consumption for data collection, they ignore the data correlation among different clusters over spatial dimension. Actually, the discovery and utilization of spatial correlation among different clusters can further increase the compression rate (or improve the recovery effect). In this paper, we propose a layered compression scheme for efficient data collection of sensory data (LCS-EDC). In the proposed scheme, first, we design a multi-layer network architecture to support the exploration of spatio-temporal correlations, especially for exploring the spatial correlation among different clusters. And then, we construct the specific projection methods respectively for exploring the temporal correlation in sensory nodes, spatial correlation (intra-cluster) in cluster heads and spatial correlation (inter-cluster) in processing nodes. Meanwhile, the detailed solving method is developed to recover original data and achieve the approximate data collection in sink node. Finally, simulation results indicate that the proposed layered compression scheme has better recovery performance as compared with traditional clustered compression schemes (i.e., achieving efficient data collection with high quality).

Device-to-device (D2D) communication is a promising technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem in D2D underlay communication with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D user equipment (DUE) and cellular user equipment (CUE) are taken into consideration under the control of proposed efficiency functions. Problems are formulated as a series of utility functions built on the unit price of jamming power and the amount of jamming service. Extracting system model into a price negotiation under Bargaining Game (PNBG) that a buyer and a seller both desiring maximum its profits, we solve the problems by reaching an agreement of the two sides. The step number of bargain process is also a restriction under consideration. For the Non-Step scheme, an Evaluation Function (EF) and a Comprehensive Utility Function (CUF) are demonstrated to analyze the negotiation process. For Step-Contained scheme, the step number of iteration is involved and an Attenuation Function (AF) is introduced to modify the Bargaining Game. Algorithms of two schemes are designed to derive the equilibrium point for reaching an agreement. Finally, simulations are illustrated for verifying proposed approach.

According to the practical requirements of high recovery precision and low latency in wireless big sensory data networks, this paper proposes an accelerated distributed rate control method for minimizing the recovery error of big sensory data. This method can guarantee the error minimization of reconstructed data and converge to the optimal value fast with a lower latency. In order to achieve these effects, an accelerated distributed solving algorithm is constructed by designing accelerated subgradient method for dual decomposition. This solving algorithm achieves convergence rate O(1/t2) in practical implementation, which significantly improves the convergence rate of regular solving algorithms. Meanwhile, the convergence analysis testifies the convergence property of the proposed distributed solving algorithm, and this algorithm is applicable to other convex optimization problems. Finally, the performance evaluation shows that the proposed accelerated method can converge to the unique optimal value successfully and the convergence speed is faster than the regular optimization method, and this proposed method can be extended to networks of different sizes without sacrificing the accelerated effect.

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.

To protect user privacy in mobile peer to peer (MP2P) networks, a network coding-based mutual anonymity communication protocol (NMA) is proposed. Our contributions are described as below. We first design a network coding scheme which can defend against various omniscient adversary attacks. Then a novel anonymous communication protocol is presented to meet the anonymity requirement for MP2P applications. The novel anonymous communication protocol is comprised of three steps: query issuance, reply-confirm and file delivery. They all employ the network coding scheme to split and encrypt the signaling and data information. The splitted fragments are flooded at a certain number of hops until some intermediate peers called agents, can collect enough fragments to recover the original information. Next, the agents forward the messages to their neighboring peers. For the query issuance, the neighboring peers forward the query message to the responders by random walk mechanism. For the rest steps, the data information is delivered along the reversed paths discovered by the way of onion routing plus buffer information in routing table. In the entire process, the identities and sensitive information about the initiator and responder are completely hidden. The advantages of the scheme lie in the fact that the network coding and mutli-agent can improve the load balance, the successful rate of information transmission and anonymity degree. The experimental results demonstrate that when the percentage of malicious peers is lower than 50%, the various performances of the NMA, including the response time and the success rate, outperform other mutual anonymity schemes.

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.