Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without comprom...
Efficient communication in the Internet of Things (IoT) is essential for enabling smart applications. While NFC excels in near-field device interaction, its limited communication range hinders LoRa’s long-range communication due to its low data...
With the increasing importance of reduced-energy wireless communication in 5G networks, maximizing the energy efficiency of device-to-device (D2D) communication has gained a lot of research interest. But so far, the methods have only been able to add...
This paper proposes a secure routing protocol based on an ad hoc on-demand distance vector to simultaneously achieve communication efficiency and security. Many studies have discussed secure protocols. However, conventional protocols tend to exhibit...
With the expansion of the Internet-of-Things, energy-efficient communication is becoming vital. The communication among energy-limited devices (e.g., powered by batteries or harvesting the energy from their environment) must be energy-efficient, prol...
While the languages of the world vary greatly, they exhibit systematic patterns, as well. Semantic universals are restrictions on the variation in meaning exhibit cross-linguistically (e.g., that, in all languages, expressions of a certain type can o...
Distributed learning has received increasing attention in recent years and is a special need for the era of big data. For a support vector machine (SVM), a powerful binary classification tool, we proposed a novel efficient distributed sparse learning...
In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of...
Federated learning (FL) strikes a balance between privacy preservation and collaborative model training. However, the periodic transmission of model updates or parameters from each client to the federated server incurs substantial communication overh...
Multi-agent reinforcement learning has emerged as a promising paradigm for cooperative unmanned aerial vehicle (UAV) swarm coordination. However, existing communication-aware MARL methods primarily focus on communication topology, message routing, an...
The significance of vehicle-to everything (V2X) communication in ensuring road safety is undeniable. In addition, real-time vehicle communication requires an ample amount of spectrum resources. However, the existing spectrum resources are seriously s...
This paper investigates point-to-point multimodal digital semantic communications in a task-oriented setup, where messages are classified at the receiver. We employ a pre-trained transformer model to extract semantic information and propose three met...
The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and com...
To address the challenge of collaborative strategy optimization caused by non-independent and identically distributed data in cross-institutional scenarios, a federated quantitative learning framework integrating Path Attention Aggregation Module (PA...
The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource c...
In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, a...
The rapid expansion of smart city infrastructures and Internet of Things (IoT) networks has led to extremely dense wireless deployments, driving unsustainable energy consumption and exacerbating environmental concerns. To improve sustainability in th...
The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence al...
In this paper, an energy efficiency (EE)–spectral efficiency (SE) trade-off scheme is investigated for the distributed reconfigurable intelligent surface (RIS)-assisted backscatter vehicle-to-infrastructure (V2I) communication system. Firstly,...
Vertical Federated Learning (VFL) is a promising category of Federated Learning that enables collaborative model training among distributed parties with data privacy protection. Due to its unique training architecture, a key challenge of VFL is high...
In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia del...
With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probabilit...
One of the major concerns in wireless sensor networks (WSNs) is most of the sensor nodes are powered through limited lifetime of energy-constrained batteries, which majorly affects the performance, quality, and lifetime of the network. Therefore, div...
Federated learning (FL) enables privacy-preserving collaborative training by iteratively aggregating locally trained model parameters on a central server while keeping raw data decentralized. However, FL faces critical challenges arising from data he...
In the last decade, the advances in optoelectronics have led optical wireless communication to become an effective technology to transmit data in challenging environments like the underwater one. Unlike the well-established acoustic signaling, underw...
This paper examines reliable and energy-efficient transmission in low earth orbit (LEO) satellite communication systems. In particular, we analyze the link transmission characteristics of the LEO satellite to the ground user and model the channel as...
Multi-radar systems can significantly improve tracking robustness and accuracy, but practical deployments are challenged by asynchronous sensing timestamps across distributed platforms and by limited communication bandwidth. This paper proposes a com...
In large-scale wireless sensor networks (WSNs), nodes close to sink nodes consume energy more quickly than other nodes due to packet forwarding. A mobile sink is a good solution to this issue, although it causes two new problems to nodes: (i) overhea...
Visible light communications (VLCs) utilizing multi-color light-emitting diodes (LEDs) can achieve a high modulation bandwidth and high-quality illumination compared with phosphor-converted LEDs, which have attracted much attention. However, the spec...
Specializing Directed Acyclic Graph Federated Learning (SDAGFL) is a new federated learning framework with the advantages of decentralization, personalization, resisting a single point of failure, and poisoning attack. Instead of training a single gl...
Federated learning enables collaborative model training without centralizing private data, yet its deployment on edge devices remains constrained by communication overhead, heterogeneous data distributions, and privacy leakage from shared model updat...
The object of this research is resource management and energy consumption processes in optical fiber communication networks with access–metro–core architectures. The study addresses the problem that conventional static and semi-dynamic co...
Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health,...
This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages...
Heterogeneous client distributions and differential-privacy noise can slow learning. This study proposes time-varying privacy-scheduled differentially private federated learning (TVP-DPFL), combining a public Gaussian multiplier schedule, bounded rel...
This paper proposes utilizing federated learning (FL), a distributed learning paradigm, to process large, decentralized, and heterogeneous edge data in the context of Internet of Things (IoT) devices. However, heterogeneity and high communication cos...
Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power...
Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. T...
It is crucial to design new communication technologies to surmount the setbacks in RF communication systems. A suitable energy-efficiency scheme helps evade needless energy consumption in wireless communication. Appropriate choice of the most suitabl...
Temporal interaction streams are often distributed across data owners, while downstream labels and communication budgets remain limited. Existing temporal graph prompts typically adapt at a centralized trainer, whereas federated graph methods commonl...
Federated learning trains shared models without centralizing local data, but repeated model exchange can become a bottleneck in bandwidth-, energy-, and latency-constrained edge systems. This paper presents FEDCODE, a communication-aware federated le...
Distributed edge sensing systems, such as IoT monitoring nodes, wearable devices, and camera-based sensing terminals, continuously generate privacy-sensitive data that are costly to transmit to a central server. Federated learning (FL) provides a pro...
16 October 2018
The objective of this paper is to analyze the provincial efficiency of the Chinese community health care service and its differences. This study allows us to predict the provincial differences in the efficiency of the Chinese community health care se...
Cooperative perception can improve the situational awareness of connected and autonomous vehicles by exchanging complementary sensing information among nearby agents. However, systematic multi-vehicle cooperation also increases vehicle-to-everything...
Language evolution is driven by pressures for simplicity and informativity; however, the timescale on which these pressures operate is debated. Over several generations, learners’ biases for simple and informative systems can guide language evo...
This Special Issue on “Secure and Efficient Communication in Smart Grids” received a total of 11 submitted articles, of which 5 were accepted and published after each passing an independent peer-review process [...]
To simultaneously address dual demands on the spectral efficiency and the data transmission rate of maritime communications, in this paper, a novel maritime communication technology based on time-delay overlapping modulation (TDOM) is proposed. In TD...
Machine-to-Machine (M2M) communications enable networked devices and services to exchange information and perform actions seamlessly without the need for human intervention. They are viewed as a key enabler of the Internet of Things (IoT) and ubiquit...
Unmanned Aerial Vehicles (UAVs) have been regarded as a promising means to reshape future wireless communication systems. In this paper, we consider how to plan the trajectory of a solar-powered UAV under a cloudy condition to secure the communicatio...
Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security...
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