Central Server Research Articles

FedCust: Offloading hyperparameter customization for federated learning

Federated Learning (FL) is a new machine learning paradigm that enables training models collaboratively across clients without sharing private data. In FL, data is non-uniformly distributed among clients (i.e., data heterogeneity) and cannot be redistributed nor monitored like in conventional machine learning due to privacy constraints. Such data heterogeneity and privacy requirements bring new challenges for learning hyperparameter optimization as the training dynamics change across clients even within the same training round and they are difficult to be measured due to privacy. The state-of-the-art in hyperparameter customization can greatly improve FL model accuracy but also incur significant computing overheads and power consumption on client devices, and slowdown the training process. To address the prohibitively expensive cost challenge, we explore the possibility of offloading hyperparameter customization to servers. We propose FedCust, a framework that offloads expensive hyperparameter customization cost from the client devices to the central server without violating privacy constraints. Our key discovery is that it is not necessary to do hyperparameter customization for every client, and clients with similar data heterogeneity can use the same hyperparameters to achieve good training performance. We propose heterogeneity measurement metrics for clustering clients into groups such that clients within the same group share hyperparameters. FedCust uses the proxy data from initial model design to emulate different heterogeneity groups and perform hyperparameter customization on the server side without accessing client data nor information. To make the hyperparameter customization scalable, FedCust further employs a Bayesian-strengthened tuner to significantly accelerates the hyperparameter customization speed. Extensive evaluation demonstrates that FedCust achieves up to 7/2/4/4/6% better accuracy than the widely adopted one-size-fits-all approach on popular FL benchmarks FEMNIST, Shakespeare, Cifar100, Cifar10, and Fashion-MNIST respectively, while being scalable and reducing computation, memory, and energy consumption on the client devices, without compromising privacy constraints.

Scaling social impact. A framework for collaboration between social enterprises and for-profit enterprises

Purpose The purpose of this study is to contribute to a better understanding of innovative forms of collaboration between different types of enterprises – aimed at scaling social impact – and address the challenges and complexities inherent to these specific types of partnerships. The particular focus is on strategic collaboration between work-integration social enterprises (WISEs) and mainstream, or for-profit enterprises (FPEs) with the shared objective to create more and better employment opportunities for disadvantaged individuals in the labour market. Design/methodology/approach This study used a qualitative research design. The total sample consisted of 16 small- and medium-sized enterprises (both WISEs and FPEs), which were selected for their proven, business-to-business revenue model and their explicit ambition to create more inclusive jobs for disadvantaged individuals. Data collection and analysis took place between 2021 and 2023 and consisted of: semi-structured interviews with representatives of the participating enterprises to get a better understanding of the way in which current partnerships operate; and co-creative research methods to facilitate change processes – within and outside these partnerships – aimed at creating more social impact. Findings Most collaborations between WISEs and FPEs start purely transactional, with the exchange of products or services, but once they become more familiarised with each other, the realisation of (joint) social impact becomes more significant. The ambition to further coordinate and integrate operations is prominent, but the partnership process is not without challenges and requires time, commitment and trust. So far, only few collaborations can be considered truly transformational. Originality/value This study contributes to the discussion on strategic alliances and cross-sector collaborations by providing a conceptual framework and a practical instrument to shape strategic collaboration between social enterprises and FPEs that aim to create more social impact.

Privacy-preserving and communication-efficient stochastic alternating direction method of multipliers for federated learning

Federated learning constitutes a paradigm in distributed machine learning, wherein model training unfolds through the exchange of intermediary results between a central server and federated clients. Given its decentralized nature, conventional machine learning algorithms find limited applicability in the context of federated learning models. Hence, the alternating direction method of multipliers (ADMM), tailored for distributed optimization, is leveraged for this purpose. However, despite the considerable promise of the ADMM algorithm in federated learning, it faces challenges related to computational efficiency, communication efficiency, and data security. In response to these challenges, this study proposes the privacy-preserving and communication-efficient stochastic ADMM (PPCESADMM) algorithm that enhances the computational efficiency through the stochastic optimization method, reduces communication costs through sparse communication method, and ensures the security of federated clients' data via the homomorphic encryption method. Theoretical analyses confirm the convergence of the PPCESADMM algorithm under mild conditions and establish its convergence rate as O(1/T). Experiments illustrate the superior performance of our algorithm in communication cost compared to ADMM and CEADMM algorithms, achieving reductions of 65.10% and 44.32%, respectively. Furthermore, our method surpasses classical federated learning algorithms such as FedAvg, FedAvgM, and SCAFFOLD in terms of algorithmic convergence, achieving superior convergence precision within predefined training epochs. Finally, our algorithm converges to the same results as those obtained without using homomorphic encryption, albeit at the cost of increased computation time.

Fault diagnosis method for elevator carriages based on temporal generative federated distillation

Addressing the ‘data silo’ issue among different elevator operating units and the temporal correlations in elevator vibration signals, a novel small-sample fault diagnosis method for elevator carriages based on temporal generative federated distillation is proposed. This method incorporates a temporal generative adversarial network into Federated Distillation via Generative Learning (FedGen). FedGen combines federated learning, knowledge distillation, and generative models to enhance model aggregation efficiency while mitigating data heterogeneity. However, the original generative model struggles to maintain dynamic correlations between signals when extracting temporal features. Therefore, an improved Time Series Generative Adversarial Networks (TimeGAN) model is introduced, substituting the initial logarithmic loss function with a least squares error function, thereby enhancing training stability and data quality. This approach eliminates the need for proxy datasets in knowledge distillation, avoiding the loss of temporal information during central server feature extraction. Simulation results demonstrate that this method enables data sharing while protecting data privacy, and enhances model generalization capabilities.

HPB SO06 - An advanced clinical practitioner (ACP) led pancreatic cyst surveillance programme

Abstract Background The prevalence of pancreatic cysts (PCs), including intraductal papillary mucous neoplasms (IPMNs), has increased substantially over recent decades. This rising prevalence is due largely to the increasing volume and quality of medical imaging technology. Although PC's are often an incidental finding in asymptomatic individuals, a small number do harbour a certain malignant potential and thus raise warranted concern. The challenge therefore is identifying the high-risk lesions for early resection through robust, guideline-driven surveillance. To ensure a consistent and evidence-based approach to PC surveillance, our trust has adopted a dedicated surveillance programme led by an Advanced Clinical Practitioner (ACP) in gastroenterology. Method A PC surveillance clinic was designed and delivered from April 2023. The service underwent a formal inauguration and was promoted at the regional HPB MDT. All patients receive a standardised approach to surveillance in-line with the current international guidelines or regional MDT recommendation. Surveillance diagnostics are arranged by the ACP and patients are updated with regard to their results and next steps during a telephone or face-to-face consultation. A robust database was designed to capture relevant patient details and serve as a future research aid. Results 144 referrals have been received (04/23-04/24). 134 are undergoing surveillance, with discontinuation due to frailty, patient choice, diagnosis of an inflammatory cyst or relocation in 10 cases. Most PCs under surveillance are side-branch IPMNs (70.9%). Malignant transformation has not yet been observed, however 18 cases display worrisome features and 3 have high-risk stigmata. These are undergoing intensive surveillance or awaiting MDT discussion. 93% of surveyed patients (16) report a greater sense of involvement in their PC surveillance and a greater awareness of their surveillance protocol (93%). Referrer feedback (7) has been positive and staff are satisfied with this service (100%). Conclusion Robust surveillance of patients with PC will likely improve early detection and prognosis of pancreatic cancer by enabling early identification of resectable cases. A centralised service that adopts a single agreed guideline will reduce variation in the management of these cysts, ensure these complex lesions are under specialist observation and offer a single point of contact which will improve retainment and follow-up of these patients. It is intended that synchronous data collection will serve to generate a comprehensive repository of information relating to PC. This databases will aid future research into this evolving field and steer future guideline development.

An intelligent agriculture monitoring framework for leaf disease detection using YOLOv7

Agriculture is one of the most important economic sectors on which societies have relied since ancient times. With the recent development of technology, agriculture has also been incorporating modern techniques such as the Internet of Things and Artificial Intelligence to improve productivity and monitor the farming process. One of agriculture’s most prominent issues is the spread of plant diseases and the lack of real-time monitoring. Various systems and operations have recently been developed to predict and diagnose plant diseases. However, current operations have been selective, focusing on a specific aspect without addressing other important aspects, resulting in either partial or compound application of results, rendering the desired outcomes ineffective. To deal with such challenges, we propose an intelligent framework for real-time agriculture monitoring and disease detection, namely a system for monitoring plant diseases using YOLOv7. In the proposed framework, a rule-based policy has been designed for detecting plant diseases using online plant leaf monitoring, sensors, and surveillance cameras. Images of plant leaves captured by different cameras are sent in real-time to central cloud servers for disease detection. The improved YOLOv7 technology is utilized for plant disease detection, and the proposed system has been evaluated using a dataset of diseased tomato leaves, comparing it with different models based on various performance metrics to demonstrate its effectiveness, achieving an accuracy of 96%.

Development of a Smart Lock System using QR Code Technology

A smart lock system refers to a modern security solution that enables users to remotely control access to their homes or businesses using a smart device such as smartphone, tablet, or computer. This paper presents the development of a smart lock system using Quick Response (QR) code technology to address the problem of insecurity, inconvenience, and inflexibility in access control. The proposed system eliminates the need for physical keys or complex authentication and verification methods. The system integrates an ESP8266 microcontroller, barcode scanner, 16x2 liquid-crystal display (LCD), solenoid lock, relay, and a 12V power supply. The system allows authorised users to generate unique QR codes representing their access credentials through a user-friendly interface. These QR codes are scanned by the system, which processes the codes, verifies access permissions via a central server and provides real-time feedback through the LCD. Upon successful verification, the system triggers a relay to control the solenoid lock, to grant or deny access. The implementation and testing of the smart lock system demonstrated successful operation. The performance indices include authentication speed, system reliability, accuracy and response time, which were evaluated and found to be satisfactory. The obtained results showed that the proposed system outperformed traditional methods such as keys, and fingerprint authentication, with an access speed of 2.00 seconds, which is significantly faster than the alternatives. Comparative analysis with existing access control techniques demonstrated the developed system's enhanced security features and user-friendly operation, making it applicable for residential, commercial, and industrial use. Additionally, the system's scalability and adaptability allow it to be customised for various environments, offering a versatile solution to diverse security challenges. This work contributes to the field of security technology by providing an efficient and scalable smart lock system with novel algorithms, which enhances the convenience, robustness of access control and seamless user experience.

Unifying domain gap in Federated Learning: A geometric approach

Federated Learning (FL) is a framework designed to enable distributed and privacy-preserving learning. It typically involves a central server coordinating multiple clients to collaboratively train a global model without the need for sharing private data. Recent studies have revealed that this parameter-only communication strategy can be hindered by non-independent and identically distributed (non-iid) data across clients, leading to statistical heterogeneity. This issue necessitates the development of Personalized Federated Learning (PFL), which aims to enhance the performance of individual clients while still allowing for the learning of a global model. Most existing work on PFL has focused on label shift in supervised tasks, with relatively little attention given to feature shift, another common problem in real-world applications of federated learning. For instance, client data may vary in terms of illumination, angles, and quality for natural images, or in imaging protocols for medical images. To address this issue, we propose a general yet powerful framework that leverages the geometric interpretation of deep neural networks. Geometrically, a deep neural network partitions the feature space into a Power Diagram (PD) or Voronoi Diagram (VD). Regularized losses are introduced to mitigate feature shift from the perspective of PD and VD. Additionally, a personalized generator network is formulated for each client to rectify feature distribution and adapt the input data from its own domain to an ”averaged” or unified domain that generalizes well across all clients. Our method demonstrates competitive performance compared to current state-of-the-art methods.

A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN.

V2X application server and vehicle centric distribution of commitments for V2V message authentication

Safety applications, such as intersection collision warnings and emergency brake warnings, enhance road safety and traffic efficiency through periodic broadcast messages by vehicles and roadside infrastructure. While the Elliptic Curve Digital Signature Algorithm (ECDSA) is a widely used security approach, its performance limitations make it unsuitable for time-critical safety applications. As such, a symmetric cryptography-based technique called Timed Efficient Stream Loss-tolerant Authentication (TESLA) offers a viable alternative. However, applying standard TESLA in the context of vehicle-to-vehicle (V2V) communications has its own challenges. One challenge is the difficulty of distributing authentication information called commitments in the highly dynamic V2V environment. In this paper, we propose two novel solutions to this problem, namely, V2X Application Server (VAS)-centric and vehicle-centric. The former is an application-level solution that involves selective unicasting of commitments to vehicles by a central server, the VAS, and the latter is a reactive scheme that involves the periodic broadcast of commitments by the vehicles themselves. Extensive simulations are conducted using representatives of the real V2V environment to evaluate the performance of these approaches under different traffic situations; as well as performance comparison with a state-of-the-art distribution solution. The simulation results indicate that the VAS-centric solution is preferable for use in a TESLA-like V2V security scheme. It demonstrates desirable features, including timely delivery of commitments and high distribution efficiency, with over 95 % of commitments sent by the VAS are associated with relevant safety messages when compared with the vehicle-centric and state-of-the-art solutions. Formal security analysis, conducted using the Random Oracle Model (ROM), proves the correctness of our proposed distribution schemes. Additionally, an informal security analysis shows the resilience of the proposed schemes against various attacks, including impersonation, replay, and bogus commitment messages.

MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare.

Federated learning (FL) has attracted increasing attention to building models without accessing raw user data, especially in healthcare. In real applications, different federations can seldom work together due to possible reasons such as data heterogeneity and distrust/inexistence of the central server. In this article, we propose a novel framework called MetaFed to facilitate trustworthy FL between different federations. MetaFed obtains a personalized model for each federation without a central server via the proposed cyclic knowledge distillation. Specifically, MetaFed treats each federation as a meta distribution and aggregates knowledge of each federation in a cyclic manner. The training is split into two parts: common knowledge accumulation and personalization. Comprehensive experiments on seven benchmarks demonstrate that MetaFed without a server achieves better accuracy compared with state-of-the-art methods [e.g., 10%+ accuracy improvement compared with the baseline for physical activity monitoring dataset (PAMAP2)] with fewer communication costs. More importantly, MetaFed shows remarkable performance in real-healthcare-related applications.

Genotypic variation in resource exchange, use, and production traits in the legume-rhizobia mutualism.

Mutualisms, reciprocally beneficial interactions between two or more species, are ubiquitous in nature. A common feature of mutualisms is extensive context-dependent variation in fitness outcomes. This context-dependency is hypothesized to stem from the environment's mediation of the relative costs and benefits associated with mutualisms. However, traits related to the exchange of goods and services in mutualisms have received little attention in comparison to net fitness outcomes. In this study, we quantified the contribution of host and symbiont genotypes to variation in resource exchange, use, and production traits measured in the host using the model mutualism between legumes and nitrogen-fixing rhizobia. We predicted that plant genotype × rhizobia genotype (G × G) effects would be common to resource exchange traits because resource exchange is hypothesized to be governed by both interacting partners through bargaining. On the other hand, we predicted that plant genotype effects would dominate host resource use and production traits because these traits are only indirectly related to the exchange of resources. Consistent with our prediction for resource exchange traits, but not our prediction for resource use and production traits, we found that rhizobia genotype and G × G effects were the most common sources of variation in the traits that we measured. The results of this study complement the commonly observed phenomenon of G × G effects for fitness by showing that numerous mutualism traits also exhibit G × G variation. Furthermore, our results highlight the possibility that the exchange of resources as well as how partners use and produce traded resources can influence the evolution of mutualistic interactions. Our study lays the groundwork for future work to explore the relationship between resource exchange, use and production traits and fitness (i.e., selection) to test the competing hypotheses proposed to explain the maintenance of fitness variation in mutualisms.

Vasectomy Discussions in South Korea: Central Themes and Public Interest on Question-and-Answer Social Platforms.

The purpose of this paper is to explore public perceptions and key issues related to vasectomy in South Korea. In particular, this paper seeks to analyze how vasectomy is discussed on question-and-answer (Q&A) social platforms including NAVER Jisik iN (knowledge exchange service between NAVER users) and to identify topics that capture public interest. In this study, based on data collected on NAVER Jisik iN from July 2019 to July 2024 with the keyword 'vasectomy,' this paper performs a co-occurrence matrix analysis on the Q&A data to identify key terms related to the topic. By applying connection centrality and community detection techniques, this paper visualizes and examines the network graph to uncover relationships between words and identify the primary topics of discussion. The co-occurrence matrix analysis reveals that keywords such as 'surgery,' 'vasectomy,' and 'contraception' play a central role in discussions about vasectomies. Both the Q&A data highlight the vasectomy procedure and contraceptive effectiveness as primary topics of discussion. The visualization graphs intuitively display the centrality and strength of connections between these keywords, as well as the interrelatedness of topics through distinct community groups. The analysis shows that the questions primarily focused on the surgical procedure, while the answers were more concerned with contraceptive effectiveness and physiological aspects. These results suggest that the public's interest in vasectomy is closely linked to its effectiveness, potential side effects, and related social issues.

Network Server Management Based on Virtualization Technology using Proxmox at Diskominfo Bengkayang Regency

Based on research conducted by the author, two server machines at Diskominfo Bengkayang District have poor resources because they use personal computers with specifications not for servers, so they cannot serve clients properly. Besides that, there is no central data storage server for sharing data. The solution to the problem of server resources is to replace it with one unit of PC Server with high specifications and because there are two servers, the method used for this problem is Virtualization Technology. All server machines are built in virtualization technology using Proxmox. Proxmox is open-source software for running Virtual Machine. With proxmox, it can minimize the use of hardware and facilitate maintenance because it uses Web Base Management for its settings and with the construction of a data center server for value data storage and data sharing, will provide convenience in doing work. The benefits of this community service activity are to streamline time and costs in server maintenance and optimal use of resources.

Indicators of Attracting Foreign Direct Investment in Developing Countries: Evidence from Vietnam

In today’s global economy, whether in advanced or emerging markets, the importance of being open to trade and investment is widely acknowledged as a key driver of economic growth. This economic openness is primarily demonstrated through the unrestricted exchange of goods and services as well as the free movement of international capital. While previous studies have investigated the effects of export-driven, import-driven, and capital-driven growth strategies, this research focuses on analyzing the impact of each indicator of the Provincial Competitiveness Index (PCI) on attracting foreign direct investment (FDI). By utilizing a panel data model covering 63 provinces and cities in Vietnam from 2018 to 2022, this study explores the correlation between PCI, FDI, and economic expansion. Findings suggest that export-oriented growth policies can be more effective in fostering the development of Asian emerging economies than FDI-centric approaches. Nonetheless, FDI continues to be crucial for technology transfer, capital accumulation, and knowledge exchange in management. Regions with advanced economies are more appealing to FDI, while provinces with distinctive industrial strengths also attract foreign investment. To further boost FDI inflows, it is advised that the government streamline market entry procedures, reduce administrative and tax burdens, increase transparency, and promote fair competition. These measures are anticipated to improve the investment climate and enhance competitiveness on the global stage.

A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network

The increasing demand for processing numerous data from IoT devices in a hierarchical IoT network drives researchers to propose different resource allocation methods in the edge hosts efficiently. Traditional approaches often compromise on one of these aspects: either prioritizing local decision-making at the edge, which lacks global system insights or centralizing decisions in cloud systems, which raises privacy concerns. Additionally, most solutions do not consider scheduling tasks at the same time to effectively complete the prioritized task accordingly. This study introduces the hierarchical adaptive federated reinforcement learning (HAFedRL) framework for robust resource allocation and task scheduling in hierarchical IoT networks. At the local edge host level, a primal–dual update based deep deterministic policy gradient (DDPG) method is introduced for effective individual task resource allocation and scheduling. Concurrently, the central server utilizes an adaptive multi-objective policy gradient (AMOPG) which integrates a multi-objective policy adaptation (MOPA) with dynamic federated reward aggregation (DFRA) method to allocate resources across connected edge hosts. An adaptive learning rate modulation (ALRM) is proposed for faster convergence and to ensure high performance output from HAFedRL. Our proposed HAFedRL enables the effective integration of reward from edge hosts, ensuring the alignment of local and global optimization goals. The experimental results of HAFedRL showcase its efficacy in improving system-wide utility, average task completion rate, and optimizing resource utilization, establishing it as a robust solution for hierarchical IoT networks.

Indoor Air Quality System: A Design of Monitoring CO and CO2 Substances for Elderly Home Using IoT

The use of an IoT-based monitoring system for CO and CO2 gases in elderly homes is a necessary innovation for remotely monitoring air quality. This work was successful in establishing a remote monitoring system that can measure CO and CO2 gases utilizing IoT technology combined with Blynk software. The MQ-7 sensors detect CO gas, while the MQ-135 sensors detect CO2. Data from these sensors is delivered to a central server, which then sends it to the I2C 16X2 LCD panel and Blynk software, allowing data visualization via an internet connection. The study's findings indicate that this system can detect and monitor CO and CO2 gas levels with a high degree of precision. This device can also monitor air quality in real time using the Blynk software. As a result, users may accurately monitor air quality (CO and CO2 gases) in elderly homes.

Federated learning-based wavelength demodulation system for multi-point distributed multi-peak FBG sensors.

Machine learning-based demodulation of multi-peak fiber Bragg grating (FBG) sensors has been extensively studied, demonstrating superior performance compared to conventional algorithms because it can neglect potential physical constraints. As the number of real-world monitoring points increases, the volume of fiber-optic sensing data grows exponentially. This necessitates aggregating data from various regions (e.g., via Wi-Fi), unlike traditional single-point monitoring, which challenges server storage capacity and communication efficiency. To address these issues, this paper proposes a federated learning (FL)-based framework for efficient wavelength demodulation of multi-peak FBGs in multipoint monitoring. Specifically, an arrayed waveguide grating (AWG) with multiplexing capability is employed at different monitoring points to convert spectral features into multi-channel transmission intensities, serving as training data for local models. Subsequently, the local model parameters, trained independently at each point, are uploaded to a central server to derive the optimal global model for demodulation across different monitoring points. The proposed demodulation framework is validated through stress demodulation experiments on multi-peak FBG sensors. Experimental results indicate strong multi-peak extraction performance with a demodulation error of ±0.64 pm. Additionally, the method demonstrates excellent applicability for generating high-precision global demodulation models through multi-node cooperative work under various scenarios.

Worker Presence Monitoring in Complex Workplaces Using BLE Beacon-Assisted Multi-Hop IoT Networks Powered by ESP-NOW

The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as shipyards, large factories, warehouses, and other construction sites due to a lack of traditional network infrastructure. In this context, we developed a novel system integrating Bluetooth Low Energy (BLE) beacons with multi-hop IoT networks by using the ESP-NOW communications protocol, first introduced by Espressif Systems in 2017 as part of its ESP8266 and ESP32 platforms. ESP-NOW is designed for peer-to-peer communication between devices without the need for a WiFi router, making it ideal for environments where traditional network infrastructure is limited or nonexistent. By leveraging the BLE beacons, the system provides real-time presence data of workers to enhance safety protocols. ESP-NOW, a low-power communications protocol, enables efficient, low-latency communication across extended ranges, making it suitable for complex environments. Utilizing ESP-NOW, the multi-hop IoT network architecture ensures extensive coverage by deploying multiple relay nodes to transmit data across large areas without Internet connectivity, effectively overcoming the spatial challenges of complex workplaces. In addition, the Message Queuing Telemetry Transport (MQTT) protocol is used for robust and efficient data transmission, connecting edge devices to a central Node-RED server for real-time remote monitoring. Moreover, experimental results demonstrate the system’s ability to maintain robust communication with minimal latency and zero packet loss, enhancing worker safety and operational efficiency in large, complex environments. Furthermore, the developed system enhances worker safety by enabling immediate identification during emergencies and by proactively identifying hazardous situations to prevent accidents.

MARKETING IN THE SPHERE OF PUBLIC ADMINISTRATION

The article reveals the relevance of using marketing tools in the field of public administration. The concept of «marketing» is analyzed, the approaches of modern scientists to this concept are considered. Today, there are about 2,000 definitions of the concept of «marketing», in which the main tasks, processes and concepts of marketing are reflected in various combinations. A significant impact on the variety of definitions of marketing was made by the variety of points of view on the goals, objectives and content of marketing and marketing activities. Quite often, definitions of marketing are based on concepts of an economic nature (for example, profit maximization, increasing income, market, competitiveness, etc.), on management concepts (planning, regulation, organization, etc.). Emphasis is placed on a large number of derivative concepts of marketing, including «state marketing», «territorial marketing», «regional marketing», etc. It is indicated that marketing in the field of public administration is, first of all, an approach in public administration that puts the satisfaction of consumers and producers of public services in a prominent place, which acts as a specific form of marketing implementation. More specifically, this is the marketing activity of management bodies and/or their representatives who act as producers, consumers or intermediaries (suppliers) in the processes of exchange of goods, services, ideas and other values. The tools of state marketing were proposed, and the results of the use of the tools were analyzed. An analysis of the main types of marketing in the field of public administration was carried out: macro-marketing; direct state marketing; state internet marketing; de-marketing This classification is generally correct, as it takes into account the subject composition and various goals of marketing in the field of public administration. It has been proven that marketing in the field of public administration is based on real mechanisms of market functioning, and is also focused on achieving the maximum social effect from increasing the degree of satisfaction of the needs of the country's population. It is proven that the state uses marketing tools in its policy. However, so far it has a single character, and the use of such tools should be systematic. The application of marketing in the field of public administration and the activities of public bodies can be a means of achieving and increasing the competitiveness of both the national economy and the entire national state.