Centralized Model Research Articles

EMPIRICAL INSIGHTS INTO CENTRALIZED PUBLIC PROCUREMENT: A CASE STUDY

This study case analyzes the implementation of the centralized model of public procurement by the Executive Branch of the Brazilian State of Amapa. Therefore, there is following research question: did the implementation of the centralized model result in improvements in the public procurement process in Amapa? In addition, the adopted methodology is based on a case study, as well as on the collection of information in official systems, added bibliographical and documentary research, questionnaires and semi-structured interviews. As a result, it was found that, from a general perspective, the centralized model works well in the State, however adjustments and improvements are necessary in areas of the centralized model that still require investments, such as planning, procedural agility and articulation with some bodies and entities of State. The findings provide insights into centralized public purchases in a Brazilian state and allow rise up hypothesis about similar experiences in another places (countries, states, municipalities). The compreension about Purchase Centrals it is a relevant tool into test how governments establishment suas procurement strategies and how to maximize the public spend getting economy and celerity.

«Ukrainian crisis» of the Society of Marxist Historians

The article is devoted to the discussion that unfolded at the turn of the 1920s-30s between "Moscow" and "Ukrainian" historians on the principles of interaction between the Society of Marxist Historians at the Communist Academy and the Ukrainian Society of Marxist Historians. The study of the discussion allows us to come to the problem of building a model of interaction between the center and the regions in Soviet historical science. New documents from the Archive of the Russian Academy of Sciences demonstrate that the Moscow Society of Marxist Historians, headed by M.N. Pokrovsky set a course to build a relatively centralized model, which was opposed by Ukrainian historians who defended the principles of national communism. The confrontation was open in nature, in which Ukrainian historians relied on the leadership of the Ukrainian SSR. During the so-called "Great Turning Point", a wave of political purges swept through, which hit the Ukrainian intelligentsia hard, among others. After that, the obstacles to the Society of Marxist Historians were removed. However, it also found itself in crisis due to the death of its leader, M.N. Pokrovsky. The fruits of the discussion have already been "reaped" by other institutions: first, the Institute of History at the Communist Academy, and then the Institute of History of the USSR Academy of Sciences. The consequences of the "Ukrainian crisis" were massive. The opponents of building a centralized model of historical science, in which the republican centers were subordinate to the Moscow ones, were eliminated. This directly affected the process of creating the histories of the peoples of the USSR: the overcoming of the territorial historical and cultural approach, associated primarily with the Ukrainian political and intellectual elite, began, and projects appeared to create not individual histories of the republics, but the all-Union history of the USSR.

Exploring Perspectives of Blockchain Technology and Traditional Centralized Technology in Organ Donation Management: A Comprehensive Review

Background/Objectives: The healthcare sector is rapidly growing, aiming to promote health, provide treatment, and enhance well-being. This paper focuses on the organ donation and transplantation system, a vital aspect of healthcare. It offers a comprehensive review of challenges in global organ donation and transplantation, highlighting issues of fairness and transparency, and compares centralized architecture-based models and blockchain-based decentralized models. Methods: This work reviews 370 publications from 2016 to 2023 on organ donation management systems. Out of these, 85 publications met the inclusion criteria, including 67 journal articles, 2 doctoral theses, and 16 conference papers. About 50.6% of these publications focus on global challenges in the system. Additionally, 12.9% of the publications examine centralized architecture-based models, and 36.5% of the publications explore blockchain-based decentralized models. Results: Concerns about organ trafficking, illicit trade, system distrust, and unethical allocation are highlighted, with a lack of transparency as the primary catalyst in organ donation and transplantation. It has been observed that centralized architecture-based models use technologies such as Python, Java, SQL, and Android Technology but face data storage issues. In contrast, blockchain-based decentralized models, mainly using Ethereum and a subset on Hyperledger Fabric, benefit from decentralized data storage, ensure transparency, and address these concerns efficiently. Conclusions: It has been observed that blockchain technology-based models are the better option for organ donation management systems. Further, suggestions for future directions for researchers in the field of organ donation management systems have been presented.

Exploitation of Mining Resources in El Argar Culture: Bronze Age Metallurgy in the Hinterland of the Western Betic Cordillera (Southeastern Iberian Peninsula)

ABSTRACTThis research addresses the territorial organisation of metallurgical production during the El Argar Bronze Age (2200–1550 cal bc) in the inner areas of El Argar territory through lead‐isotope and trace element analyses of geological copper ores, archaeometallurgical remains and copper‐based artefacts. Results from 31 mineral and 35 archaeological samples suggest that the exploitation of copper resources in the studied region was significant and had a similar impact than other mining districts of El Argar territory. This, therefore, leads the hierarchical and centralised production model to be questioned. It also appears that the copper ore deposits in the coastal regions that were intensively exploited during the Copper Age were used less intensively in the El Argar period. At that time, copper was mostly procured from ore deposits in the inland areas of El Argar territory: that is, ore deposits within the Alpine orogeny hinterland (inland areas of the Betic Cordillera, from Granada to Baza). Other artefacts were sourced from outside the Alpine geological domain, but still on the fringe of El Argar territory (the foothills of the Sierra Morena‐Linares mining district) or even from ore deposits definitely outside El Argar territory itself (the Los Pedroches Variscan region and elsewhere).

Two-Dimensional Bi2Se3 Nanosheets Synthesis for Efficient Electrocatalytic Production of Ammonia from Nitrate

Ammonia (NH3), a critical component for various industries, is produced through the Haber-Bosch process, which, despite its importance, is a significant source of carbon emissions and operates on a centralized model, emphasizing the need for innovative solutions to decarbonize and decentralize its production. Electrochemical nitrate (NO3–) reduction to NH3 presents a promising alternative to the Haber-Bosch process for NH3 synthesis, with the added advantage of transforming waste into a valuable resource while mitigating water pollution concerns. However, achieving a well-defined active center and catalytic selectivity in the electrochemically driven reaction remains a significant challenge. In this study, we successfully fabricated a highly selective and active 2D Bi2Se3 catalyst, which demonstrated better performance in reducing NO3– to NH3 with a Faradaic efficiency (FE) of approximately 80% (−0.3V (RHE)) and a significant yield rate of 45mgh−1mgcat−1 (0.46 mmolh−1cm−2). Furthermore, the fabricated material exhibited exceptional stability and durability, maintaining a high NO3– reduction of 80% FE even after 10 consecutive cycles of extended use and continuous operation, demonstrating its remarkable resilience and reliability. Our findings show that the prepared catalyst selectively promotes the electrochemical reduction of NO3– to NH3 while suppressing the competing hydrogen evolution reaction (HER). The charge density profile indicates a pronounced charge localization around the Se atoms, implying that the Se active sites in the 2D Bi2Se3 catalyst act as the active center for the NO3– reduction mechanism, playing a crucial role in facilitating the reaction kinetics.

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and monitoring. Approaches for implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. Keywords—Machine Learning Operations (MLOps), Scalable AI Deployment, Continuous Integration and Continuous Deployment (CI/CD) for ML, ML Monitoring and Observability, Model Reproducibility, Model Versioning and Governance, Centralized Model Registry, Responsible AI Deployment, Ethical AI Practices, Enterprise MLOps

Optimizing public sector resources: A centralized DEA model for effective allocation of shared inputs

This study presents a novel centralized Data Envelopment Analysis (DEA) model with shared inputs to optimize the allocation of public sector resources and enhance management efficiency. Recognizing the need for a comprehensive approach to public sector performance evaluation, the study integrates the strengths of the centralized DEA framework and the two-stage DEA model with shared inputs. The centralized DEA model shifts the focus from individual decision-making units to optimizing resources across the entire public sector system. This allows for the reallocation of shared inputs among decision making units, leading to potential efficiency gains and improved overall performance. Incorporating shared inputs within the centralized structure enables a more nuanced understanding of the interdependencies and interactions between distinct functions and stages within the public sector. The empirical application of the proposed model in the context of public sector management and cultural subsidies provides valuable insights. The findings highlight inefficiency and offer guidance for policymakers and administrators on optimizing shared resource use. The centralized DEA model with shared inputs serves as a practical decision-support tool, informing the development of targeted policies and strategies to enhance the efficiency and effectiveness of public service delivery, particularly in resource-constrained environments. This research contributes to public sector performance evaluation's theoretical and methodological advancement, offering a comprehensive framework for resource optimization and improved management practices.

NANOPORE-BASED INTRA-OPERATIVE CLASSIfiCATION OF CENTRAL NERVOUS SYSTEM TUMOURS IN THE NHS

Abstract AIMS The 2021 WHO classification of CNS tumours necessitates the use of molecular testing in order to reach a confident diagnosis of many tumour types. At present, this is achieved using array-based technology delivered by a centralised model. This approach has inherent delays that mean it is often several weeks before the treating clinical team is informed of diagnosis, delaying the start of adjuvant therapy and causing significant distress for patients. As such, there is an urgent need to rethink this pathway and improve time to diagnosis. Nanopore longread sequencing technology has the potential to enable a paradigm shift in diagnostic and therapeutic pathways, offering rapid, comprehensive molecular diagnosis that can be performed in the intra-operative setting. We aimed to test the hypothesis that intra-operative methylation-based classification can be performed using nanopore sequencing and thereby expediently inform surgical and oncological decision-making. METHOD Oxford Nanopore Technology (ONT) is a native-strand, long-read sequencing platform that can perform copy- number profiling along with parallel single-gene methylation, structural variant, mutational and methylation analyses. Capital and consumable costs are relatively low, and testing can be performed in the local setting on single samples. We have optimised DNA extraction and library preparation for rapid tissue processing. We have developed a robust bioinformatic pipeline which is able to produce intraoperative classification within minutes of sequencing and full molecular profiling after 24 hours. RESULTS We demonstrate the feasibility of using ONT to deliver intra-operative methylation-based classification of CNS tumours in ‘realtime’ within an NHS hospital setting and will discuss the challenges of implementing this testing and the opportunities to inform neuro-oncological practice. CONCLUSION By giving a summary of our novel protocol of rapid DNA extraction, library preparation and bioinformatic analysis, we will show that this approach can revolutionise the diagnosis of brain tumours and has the potential for rapid adoption by non-expert laboratories.

Control Performance Assessment of a Zero Harmonic Distortion Grid-Forming Converter for Medium Voltage Islanded Microgrids

The world is currently witnessing a rapid transformation in the production and utilization of electrical energy. The traditional centralized generation model for electric power is swiftly evolving into a more decentralized system, known as Distributed Generation (DG), which incorporates renewable energy sources situated closer to end-users. This shift towards DG has paved the way for the emergence of grid-forming converters, which play a pivotal role in enhancing voltage and frequency stability within microgrids (MGs) and isolated applications. This study focuses on assessing the performance of the Zero Harmonic Distortion (ZHD) in both stand-alone and parallel operation modes. The distinctive feature of this converter lies in its inherent ability to generate a sinusoidal voltage source without the need for capacitive filtering components, which can adversely affect cost, efficiency, and size while potentially contributing to resonance problems. This is achieved through a judicious combination of harmonic cancellation within a three-winding transformer and the utilization of Selective Harmonic Elimination Pulse Width Modulation (SHE PWM) dismissing a closed-loop control structure. Simulation and hardware-in-the-loop results presented in this work demonstrate the satisfactory performance of the ZHD grid-forming converter.

Federated learning of medical concepts embedding using BEHRT.

Electronic health record data is often considered sensitive medical information. Therefore, the EHR data from different medical centers often cannot be shared, making it difficult to create prediction models using multicenter EHR data, which is essential for such models' robustness and generalizability. Federated learning (FL) is an algorithmic approach that allows learning a shared model using data in multiple locations without the need to store all data in a single central place. Our study aims to evaluate an FL approach using the BEHRT model for predictive tasks on EHR data, focusing on next visit prediction. We propose an FL approach for learning medical concepts embedding. This pretrained model can be used for fine-tuning for specific downstream tasks. Our approach is based on an embedding model like BEHRT, a deep neural sequence transduction model for EHR. We train using FL, both the masked language modeling (MLM) and the next visit downstream model. We demonstrate our approach on the MIMIC-IV dataset. We compare the performance of a model trained with FL to one trained on centralized data, observing a difference in average precision ranging from 0% to 3% (absolute), depending on the length of the patients' visit history. Moreover, our approach improves average precision by 4%-10% (absolute) compared to local models. In addition, we show the importance of the usage of pretrained MLM for the next visit diagnoses prediction task. We find that our FL approach reaches very close to the performance of a centralized model, and it outperforms local models in terms of average precision. We also show that pretrained MLM improves the model's average precision performance in the next visit diagnoses prediction task, compared to an MLM without pretraining.

From Protectionist to Regulator: Policy-Driven Transformation of Digital Urban Networks in China’s Online Gaming Industry

In the digital era, data-driven production organizes digital urban networks. This study explores the critical role of government policies in shaping these networks, focusing on China’s evolving policy contexts. While existing research has mainly emphasized qualitative analyses, this paper quantitatively assesses the impact of policy changes on digital urban networks, specifically through the lens of China’s online gaming industry. The study aimed to elucidate the relationship between the policy environment and digital urban networks. By examining China’s transition from protectionist to regulatory policies, this research employed a social network analysis and valued exponential random graph models (ERGMs) across two key phases: the competitive protection phase (2014–2017) and the systematic regulatory phase (2018–2022). The findings revealed a significant transformation in urban network structure, shifting from a centralized model dominated by a few core cities to a decentralized, multi-centered network. The key factors influencing this evolution include the institutional proximity and cross-regional collaborations. This study offers valuable insights into how policy shifts affect urban networks in the digital economy, contributing both theoretically and practically to future policy design.

Securing the IoT Edge Devices Using Advanced Digital Technologies

As the IoT ecosystem continues to grow, edge computing is becoming essential for handling and analyzing the vast amount of data generated by connected devices. Unlike traditional centralized data models, where information is sent to remote centers for processing, edge computing processes data closer to where it is generated. This decentralized approach helps reduce latency, optimizes bandwidth usage, and improves both privacy and security. However, the rise in IoT devices and the spread of edge computing also increase the potential for cyberattacks, demanding more robust security measures. With AI and machine learning being utilized to analyze IoT data, edge computing facilitates this analysis directly at the data source, pointing to a future where AI and ML applications are more prevalent on edge devices.

Privacy Preserving Technology using Federated Learning and Blockchain in protecting against Adversarial Attacks for Retinal Imaging

PurposeCollaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy enhancing technology that allows collaboration while respecting privacy via the development of models without raw data transfer. However state-of-the-art FL models still face challenges in non-independent and identically distributed (non-i.i.d.) health care settings and remain susceptible to privacy breaches. We propose a novel FL framework coupled with blockchain technology to address these challenges. DesignRetrospective multicohort study Subjects and Methods27,145 images from Singapore, China and Taiwan were used to design a novel FL aggregation method for the detection of myopic macular degeneration (MMD) from fundus photographs and macular disease from optical coherence tomography (OCT) scans in feature distribution skew and label distribution imbalance scenarios. We further performed adversarial attacks (label flipping and clean label). As proof of concept, blockchain was incorporated into FL to demonstrate secure transfer of model updates across collaborating sites. Main Outcome MeasuresWe evaluated our FL model performance in MMD and OCT classification and compared our model against state-of the-art FL and centralized models. ResultsOur FL model showed robust performance with areas under the receiving operating characteristic curves (AUC) of 0.868±0.009 for MMD detection and 0.970±0.012 for OCT macular disease classification. In label flipping attack, our FL model had an AUC of 0.861±0.019, similar to the centralized model (AUC 0.856± 0.015) and higher than other FL models (AUC 0.578–0.819) In clean label attack, our FL model had an AUC of 0.878±0.006 which was comparable to the centralized model (AUC 0.878±0.001) and superior to other state-of-the-art FL models with AUC of 0.529-0.838. Simulation showed that the additional time with blockchain in one global epoch was around 5 seconds. The addition of blockchain to the FL framework was feasible with a minimal impact on model development time. ConclusionsOur proposed FL algorithm overcomes the shortcoming of the traditional FL in non i.i.d. situations and remains robust to against adversarial attacks. The integration of blockchain adds further security during the transfer of model updates. Blockchain-enabled FL can be a trusted platform for collaborative health AI research.

A Multi-Agent Reinforcement Learning Blockchain Framework for Improving Vehicular Internet of Things Cybersecurity

The Vehicular Internet of Things (VIoT) is a novel idea in the field of connected transportation systems that defines a new paradigm. Nevertheless, even the most modern and complex system will require additional and more powerful layers to protect the conversation from interception and the data from leakage. The centralized models have problems like trust problems and that there might be vulnerabilities and that is why there are attempts to integrate decentralization in operation. The first challenge in the VIoT networks is that the security and openness of such a connection and data transfer are still not well developed. Another issue is the security and dependability of the interaction between the vehicles and the infrastructure, although this issue is magnified by the size of the VIoT network. This research is a blockchain and game theory base research that uses Multi-Agent Reinforcement Learning (MARL) to improve the security and efficiency of the VIoT ecosystem. The technology of blockchain gives a distributed ledger where data cannot be altered or erased. Moreover, the MARL architecture allows for the realisation of better decisions for each of the members of the network. To this, the set that was made up of the smart contracts, Vehicle Units (VUs) and the decentralized servers that form the proposed architecture would be added to allow for the right flow and processing of the data. The blockchain’s decentralized nature provides a guarantee for all secure, immutable data transfers and transparent transactions throughout the network of the VIoT. MARL enables agents to learn and acquire the best strategies as they pass through time, which leads to secure and effective communication among entities. Besides, the implementation of lightweight cryptography techniques and strategic selections according to game theory help to protect and improve the performance of the security system of the VIoT ecosystem.

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.

Exploring Egalitarianism: A Conceptual and Methodological Review of Egalitarianism and Impacts on Positive Intergroup Relations.

Considerable research on intergroup relations emphasizes attitudes, motivations, and emotions that lead to the reduction of prejudice. While factors that actively promote positive intergroup interactions have been emerging, a central theoretical framework has not been formally proposed. To address this gap, we engaged a positive psychology lens to explore how researchers have defined key indicators and consider the positive counterparts of traditional prejudice-reduction models to begin building a new framework of egalitarianism. After scanning 16,840 records that emerged in PsycInfo using keywords "egalitarian", "non-prejudice", and "intergroup", we assessed 158 articles for eligibility. Among the relevant articles (N = 54), we analyzed authors' definitions, methods of measurement, types of processes, and outcomes associated with egalitarian values as they related to intergroup behavior. Overall, there was notable variability in how researchers conceptualized and studied egalitarianism. We discuss the five broad categories of egalitarianism (prejudice reduction, universal orientation, concern for others, positive expression, and low social dominance orientation) and how they relate to positive or negative and approach or avoidant outcomes. Through these findings, we urge scholars to utilize a centralized model for studying egalitarianism in intergroup contexts moving forward.

Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction.

Diabetes is a global epidemic with severe consequences for individuals and healthcare systems. While early and personalized prediction can significantly improve outcomes, traditional centralized prediction models suffer from privacy risks and limited data diversity. This paper introduces a novel framework that integrates blockchain and federated learning to address these challenges. Blockchain provides a secure, decentralized foundation for data management, access control, and auditability. Federated learning enables model training on distributed datasets without compromising patient privacy. This collaborative approach facilitates the development of more robust and personalized diabetes prediction models, leveraging the combined data resources of multiple healthcare institutions. We have performed extensive evaluation experiments and security analyses. The results demonstrate good performance while significantly enhancing privacy and security compared to centralized approaches. Our framework offers a promising solution for the ethical and effective use of healthcare data in diabetes prediction.

Green or non‐green product: A game‐theoretic framework to make optimal decision in a two‐level supply chain

AbstractThis paper aims to discuss the decisions made by the retailer and manufacturer in a supply chain about pricing, green level, and quality of a product. The centralized model and the manufacturer‐led Stackelberg gaming model are developed for a conventional product and its environmentally friendly version. The two green models are subsequently compared with two non‐green models. Finally, a comprehensive variation of the model is considered, accounting for both green and non‐green products. The green‐only supply chain generates higher profit than the one that uses non‐green products. A revenue‐sharing contract is also employed to coordinate the supply chain.

PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients' privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient's privacy. Impact Statement-This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy.