Improve Communication Efficiency Research Articles

Medical Federated Model with Mixture of Personalized and Shared Components.

Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect five real medical datasets, including two public medical image datasets and three private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 14 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ ApplicationTechnologyOfMedicalBigData/pFedNet-code.

Enhancing vehicular NOMA communication security through reconfigurable intelligent surfaces

Physical layer security (PLS) aims to ensure the confidentiality and authenticity of transmitted data by capitalizing on the inherent randomness of wireless channels. Owing to the popularity of intelligent transportation systems (ITSs), PLS research has sparked renewed interest in the wireless research community. This paper investigates the performance of secure communication in the context of a vehicle-to-vehicle (V2V) communication scenario by employing a reconfigurable intelligent surface (RIS). Further, we introduce the concept of non-orthogonal multiple access (NOMA) to reduce latency and improve communication efficiency in V2V networks. This study aims to comprehensively analyze secrecy performance, encompassing parameters like average secrecy capacity (ASC), secrecy outage probability (SOP) and probability of non-zero secrecy capacity (PNZSC). Our research aims to highlight the efficacy of RIS in providing secure and reliable communication within V2V NOMA networks. Ultimately, our study contributes to advancing secure communication protocols in intelligent transportation systems.

Fixed-Time Distributed Event-Triggered Cooperative Guidance Methods for Multiple Vehicles with Limited Communications to Achieve Simultaneous Arrival

Aiming at the salvo-attack problem of multiple missiles, a distributed cooperative guidance law based on the event-triggered mechanism is proposed, which enables missiles with large differences in spatial location and velocity to achieve simultaneous attacks with only a few dozen information exchanges. It effectively reduces the generation of control commands and communication frequency, thereby reducing channel load and improving communication efficiency and reliability. Compared to traditional periodic sampling communication, the number of communications has been reduced by over 90%. The guidance process is divided into two stages. The first stage is the cooperative guidance stage, where missiles achieve consensus of the time-to-go estimates through information exchange. In this stage, each missile is designed with an event-triggered function based on its own state error, and the missile only updates and transmits its information in the communication network when the error meets the set threshold, effectively reducing the occupancy rate of missile-borne resources during the cooperation process. The second stage is the independent guidance stage, where missiles can hit the target simultaneously while keeping the communication network silent. This is achieved by ensuring that the time-to-go estimates of missiles can represent the real time-to-go after achieving consensus. By the design of the two-stage guidance law and the replacement of the event-triggered function, the cooperative guidance system can be ensured to remain stable in scenarios where the leader missile is present and destroyed, and this excludes Zeno behavior. The stability of the cooperative guidance law is rigorously proved by algebraic graph theory, matrix theory, and the Lyapunov method. Finally, the numerical simulation results demonstrate the validity of the algorithm and the correctness of the stability analysis.

Occlusion-guided multi-modal fusion for vehicle-infrastructure cooperative 3D object detection

In autonomous driving, leveraging sensor data (e.g. camera, LiDAR data) from both the vehicle and the infrastructure significantly improves perception capabilities. However, this integration traditionally results in increased demands on communication bandwidth. To address these challenges, we introduce Fusion2comm, an occlusion-guided feature fusion approach designed to optimize vehicle-infrastructure cooperative 3D object detection. Our innovative strategy employs an intelligent fusion of camera and LiDAR data to enhance the expressiveness of features. Subsequently, it leverages a segmentation model to extract foreground features and utilizes an occlusion-based selection of communication content, effectively easing bandwidth constraints. We propose a multimodal foreground feature fusion architecture that selectively processes and transmits critical information, substantially reducing irrelevant background data transfer. An innovative occlusion confidence-aware communication technique dynamically adjusts communication regions based on occlusion levels, ensuring efficient data exchange. Fusion2comm sets a new benchmark in the DAIR-V2X dataset, achieving an average precision of 71.25% with minimal bandwidth usage of 221.04 bytes. Our comprehensive experimental evaluations confirm that Fusion2comm substantially advances detection precision while simultaneously improving communication efficiency.

Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators.

Non-fragile cubature Kalman filtering under innovation-based weighted try-once-discard protocol with channel noise

In this paper, the problem of non-fragile cubature Kalman filtering is addressed for nonlinear systems with stochastic uncertainties under innovation-based weighted try-once-discard protocol (WTODP) affected by channel noise. To reduce transmission burden and improve communication efficiency, the innovation-based WTODP, which is constructed by a set of quadratic functions regarding innovation, is employed in the network channel between the sensor and the filter to regulate the transmission of data signals. Meanwhile, Gaussian-distributed channel noise is taken into account during the signal propagation process to provide a better representation of the network media. An upper bound of the filtering error covariance (FEC) is derived by solving certain matrix difference equations, and the minimization of the obtained upper bound is achieved through the design of an appropriate filter gain. Furthermore, the uniform boundedness of the FEC's upper bound is investigated using the matrix theory to evaluate the performance of the filtering scheme. The effectiveness and superiority of the proposed non-fragile cubature Kalman filtering algorithm are demonstrated through two simulation experiments with comparisons.

Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.

Hybrid SPEAK OUT! protocol improves aerodynamic measurements and PROMs in Parkinson's disease.

SPEAK OUT! has been shown to enhance various aspects of voice such as intensity, prosody, voice quality and perception of voice. However, their impacts on expiration and communication effectiveness have not yet been evaluated. This study aimed to evaluate the effectiveness of the Hybrid SPEAK OUT! method on aerodynamic measurements and patient-reported outcome measures (PROMs) in individuals with Parkinson's disease (PD). This study included 157 participants with PD who underwent an intensive 8-week multidisciplinary treatment program. The Hybrid SPEAK OUT! method consisted of three sessions per week, including two group sessions and one individual treatment session. Voice-related aerodynamic measures included maximum phonation time (MPT), vocal cord function using the S/Z ratio, and expiratory flow measures including peakexpiratoryflow (PEF) and peak cough flow (PCF). Two PROMs were included: the Hebrew version of the voice handicap index-10 and communication effectiveness questionnaire. The results of 111 participants were analysed. MPT duration increased, PEF and PCF increased, and better scores were found in PROMs. These findings would appear to support the effectiveness of the Hybrid SPEAK OUT! methods improving function, activity and participation in individuals with PD. However, further research is needed. What is already known on the subject Prior research has demonstrated the effectiveness of behavioral therapies, including the SPEAK OUT! program, in managing speech symptoms in individuals with Parkinson's disease (PD). These therapies have shown improvements in voice intensity, fundamental frequency, voice quality, and voice perception. However, the impact on aerodynamic measures, expiratory flow, and patient-reported outcome measures has not been extensively studied. What this paper adds to existing knowledge This study adds to the existing knowledge by demonstrating that a Hybrid SPEAK OUT! approach, can lead to improvements in aerodynamic measures, including maximum phonation time (MPT), expiratory and cough peak flow. In terms of motor learning, we found that two non-speech exhalation-related tasks that were not directly trained improved following the intensive speech training protocol. Furthermore, it shows positive changes in patient-reported outcome measures, with reduced voice-related disability and improved communication efficiency. What are the potential or actual clinical implications of this work? The findings of this study have important clinical implications for the management of speech symptoms in individuals with PD. The Hybrid SPEAK OUT! program, which combines group and individual sessions, can effectively improve voice, aerodynamic measurements and patient-reported outcome measures (PROMs), ultimately enhancing the overall quality of life for patients. These findings support the effectiveness of the Hybrid SPEAK OUT! methods for improving function, activity, and participation in individuals with PD.

KDGAN: Knowledge distillation‐based model copyright protection for secure and communication‐efficient model publishing

AbstractDeep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms.

Efficient Byzantine-robust distributed inference with regularization: A trade-off between compression and adversary

In large-scale distributed learning, the direct application of traditional inference is often not feasible, because it may contain multiple themes, such as communication costs, privacy issues, and Byzantine failures. Nowadays, the internet is vulnerable to attacks, and Byzantine failures frequently occur. For copying with Byzantine failures, the paper develops two Byzantine-robust distributed learning algorithms under a framework of communication-efficient surrogate likelihood. In our algorithms, we adopt the δ-approximate compressors, including sign-based operator and topk sparsification, to improve communication efficiency, and an unsophisticated thresholding of local gradient norms to guard against Byzantine failures. For accelerating convergence and achieving an optimal statistical error rate, error feedback is exploited in the second algorithm. The two algorithms are robust to arbitrary adversaries, although Byzantine workers don't adhere to the mandated compression mechanism. We explicitly establish statistical error rates, which imply that our algorithms don't sacrifice the quality of learning, and attain the order-optimal under some settings. In addition, we provide a trade-off between compression and adversary in the presence of Byzantine worker machines. Extensive numerical experiments validate our theoretical results and demonstrate a good performance of our algorithms.

Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

AbstractDecentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

Dinamika Bahasa Indonesia dalam Konteks Pengelolaan Perkantoran

This study aims to determine the dynamics that occur in offices in the use of Indonesian. This study uses qualitative interview method for Indonesian dynamics in the context of office management. Three interviewees from the office environment were interviewed to understand their views and experiences regarding the role of Indonesian. Data analysis is carried out qualitatively consisting of data reduction, data presentation, and conclusions. . The results of data analysis confirm that although in the office world there is already an influence from technology, Indonesian remains the main choice for communicating in the office world, without reducing the quality and diversity of Indonesian. Improve Indonesian language skills to improve communication efficiency in the workplace. The study also highlights the importance of practicing good and correct Indonesian in the office world, as it reflects professionalism and increases trust in communication. Therefore, this study highlights the need for attention to the use of good and correct Indonesian in the office environment to create effective and professional communication

The Application of Artificial Intelligence in Visual Communication in the Context of Cross-boundary Fusion

In the context of cross-border integration, significant progress has been made in the application of artificial intelligence in the field of visual communication. Through methods such as image recognition and processing, video analysis and understanding, and posture recognition and action capture, artificial intelligence not only improves the accuracy and efficiency of visual communication, but also brings innovative solutions to multiple industries. In the field of security monitoring, artificial intelligence has achieved high accuracy in facial and vehicle recognition, improving the efficiency of security monitoring; In the field of telemedicine, artificial intelligence helps doctors diagnose conditions more accurately through video analysis, improving communication efficiency; In the VR/AR field, artificial intelligence provides users with a more natural and realistic interaction experience through posture recognition and motion capture technology. These applications not only demonstrate the potential of artificial intelligence in visual communication, but also provide strong support for cross-border integration and innovative development in more fields in the future. Meanwhile, with the continuous advancement of technology and the expansion of application scenarios, we hope that artificial intelligence can play a greater role in the field of visual communication, bringing more convenience and value to society.

Empowering Communication: A Raspberry PI-Based Smart Noticeboard System

This research paper explores the implementation of a Smart Notice Board that leverages IoT and single-board computers, such as Raspberry Pi, to revolutionize traditional communication systems. It delves into the technical aspects of integrating these technologies to create a dynamic platform for real-time information dissemination. Additionally, it investigates how features like remote updating, authentication mechanisms, and environmental considerations enhance the functionality and sustainability of the Smart Notice Board. Ultimately, the paper aims to showcase the potential of this innovative solution in improving communication efficiency, security, and environmental impact within organizational and institutional settings.

Two-stage sampling with predicted distribution changes in federated semi-supervised learning

Federated semi-supervised learning (FSSL) involves training a model in a federated environment using a few labeled samples and many unlabeled samples. Compared with semi-supervised learning, FSSL faces more complex data situations, especially when data are non-independently and identically distributed (non-IID), adding more challenges to the learning process. The previous method addresses the aforementioned issues by enlarging the training sample space through multiple client random sampling and reweighting the parameters. Although it achieves high accuracy, it sacrifices communication efficiency. In this study, we propose PDCFed, a two-stage sampling method that uses the Predicted Distribution Changes of samples after different data augmentations. We evaluate the credibility of the samples based on the maximum probability predicted by weak augmentation. When the samples are in a less reliable space, they are further sampled after adjusting the predicted distribution changes using a Gaussian function. To enhance the model’s generalization ability, an entropy penalty term is incorporated after unsupervised training loss. Extensive experiments demonstrate that this method outperforms existing methods on three datasets with non-IID data and significantly improves communication efficiency.

FedCGS: Leverage Feature Compression and Conditional GAN for Privacy Protection and Efficient Communication in Federated Learning

Federated learning is a distributed machine learning technique that can train models from decentralized data, collaboratively building central models and protecting data privacy by passing partial model updates rather than raw data in each iteration of model learning. However, recent work has shown that the information during the exchange between the server and the client is still vulnerable to gradient-based privacy attacks, and traditional privacy protection methods can incur significant costs (such as communication consumption and performance evaluation). Therefore, we need an algorithm that can protect data privacy while reducing communication costs and performance losses. Here, we propose a federated learning framework FedCGS that leverages conditional generation adversarial networks and feature compression in order to achieve privacy protection while maintaining competitive model performance and effectively improving communication efficiency. We divided the local network of each client into feature extractor and public classifier, and kept the extractor local to protect privacy, and sent classifier and client generator to the server for aggregation and update through singular value decomposition, so as to improve the performance of the local network and efficient communication. A lot of experimental data prove that FedCGS can improve the performance of local model better than traditional federation learning, and improve the communication efficiency better than common conditional generation adversarial network. Therefore, we believe that FedCGS has better federal learning potential

Distributed Control for Nonlinear Time-Delay Multiagent Systems: Hybrid Saturation-Constraint Impulsive Approach.

This article mainly studies the problem of impulse consensus of multiagent systems under communication constraints and time delay. Considering the limited communication bandwidth of the agent, global and partial saturation constraints are considered. In addition, so as to further improve communication efficiency by reducing communication frequency, the novel control protocol combining event-triggered strategy and general impulse control protocol is proposed. Under this kind of novel control protocol, the communication frequency of multiagent systems can be reduced while avoiding "Zeno behavior." Through theoretical analysis, sufficient conditions for the systems to achieve consensus are obtained for the above two saturation constraint cases. In the end, the effectiveness of the novel protocols is proved by providing two different simulation instances.

EPri-MDAS: An efficient privacy-preserving multiple data aggregation scheme without trusted authority for fog-based smart grid

With the increasingly pervasive deployment of fog servers, fog computing extends data processing and analysis to network edges. At the same time, as the next-generation power grid, the smart grid should meet the requirements of security, efficiency, and real-time monitoring of user energy consumption. By utilizing the low-latency and distributed properties of fog computing, it can improve communication efficiency and user service satisfaction in smart grids. For the sake of providing adequate functionality for the power grid, various schemes have been proposed. Whereas, many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats. In this paper, we propose the EPri-MDAS: an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem, which achieves both data integrity verification and data source authentication with the most efficient block cipher-based authenticated encryption algorithm. It performs well in energy efficiency with strong security. Especially, the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses; it also allows for fault tolerance while protecting personal privacy. The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.

Harnessing ultrawideband technology for enhanced communication and radar detection

This article explores the field of pulse dynamics in functional materials with an emphasis on their use in ultrawideband technology and its significance in high-frequency situations. For thirty years, a great deal of attention in the scientific and technical literature has focused on the production of high-power Gaussian pulses, which are necessary to improve radar detection capabilities. Most notably, using avalanche transistors in conjunction with Step Recovery Diodes has been shown to be an effective way to build pulse generators that support narrow pulse widths. Also describes the complex transistor-based circuitry used to generate pulses, which is based on the avalanche mode principle and requires careful pulse shaping. The balun device is a key component of this technology since it optimizes signal integrity by converting asymmetrical pulses into balanced ones. Step Recovery Diodes are also essential for fine-tuning pulse edges, which guarantees accurate temporal properties that improve communication efficiency. This article offers insights into the transformational potential of UWB technology by offering a thorough review of the technology, including prospective applications and regulatory implications. UWB technology is ready to completely transform the field, from making old communication paradigms obsolete to bringing in a new era of unheard-of communication capabilities. All things considered, this work advances our understanding of UWB technology and pulse dynamics in a sophisticated way, making it an invaluable tool for engineers, researchers, and legislators.

Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks

Collaborative learning (CL) is a distributed learning framework that aims to protect user privacy by allowing users to jointly train a model by sharing their gradient updates only. However, gradient inversion attacks (GIAs), which recover users' training data from shared gradients, impose severe privacy threats to CL. Existing defense methods adopt different techniques, e.g., differential privacy, cryptography, and perturbation defenses, to defend against the GIAs. Nevertheless, all current defense methods suffer from a poor trade-off between privacy, utility, and efficiency. To mitigate the weaknesses of existing solutions, we propose a novel defense method, Dual Gradient Pruning (DGP), based on gradient pruning, which can improve communication efficiency while preserving the utility and privacy of CL. Specifically, DGP slightly changes gradient pruning with a stronger privacy guarantee. And DGP can also significantly improve communication efficiency with a theoretical analysis of its convergence and generalization. Our extensive experiments show that DGP can effectively defend against the most powerful GIAs and reduce the communication cost without sacrificing the model's utility.