Graph and download economic data for Secure Internet Servers for the Federated States of Micronesia (ITNETSECRP6FSM) from 2010 to 2024 about Micronesia, servers, and internet.

Secure Internet Servers for the Federated States of Micronesia was 159.81532 Number per 1 Mil. People in January of 2023, according to the United States Federal Reserve. Historically, Secure Internet Servers for the Federated States of Micronesia reached a record high of 210.87593 in January of 2019 and a record low of 9.38289 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for Secure Internet Servers for the Federated States of Micronesia - last updated from the United States Federal Reserve on August of 2025.

Graph and download economic data for Secure Internet Servers for the Russian Federation (ITNETSECRP6RUS) from 2010 to 2024 about servers, internet, and Russia.

Secure Internet Servers for the Russian Federation was 23603.59693 Number per 1 Mil. People in January of 2023, according to the United States Federal Reserve. Historically, Secure Internet Servers for the Russian Federation reached a record high of 23603.59693 in January of 2023 and a record low of 1.61080 in January of 2003. Trading Economics provides the current actual value, an historical data chart and related indicators for Secure Internet Servers for the Russian Federation - last updated from the United States Federal Reserve on July of 2025.

Data and knowledge management infrastructure for the new Center for Clinical and Translational Science (CCTS) at the University of Utah. This clinical cohort search tool is used to search across the University of Utah clinical data warehouse and the Utah Population Database for people who satisfy various criteria of the researchers. It uses the i2b2 front end but has a set of terminology servers, metadata servers and federated query tool as the back end systems. FURTHeR does on-the-fly translation of search terms and data models across the source systems and returns a count of results by unique individuals. They are extending the set of databases that can be queried.

GI-cat service instance for demo and test purposes. It implements a federation of several catalog and access servers.

The Russian data processing server market fell slightly to $2B in 2024, waning by -3.1% against the previous year. Over the period under review, consumption, however, continues to indicate a relatively flat trend pattern. Over the period under review, the market reached the maximum level at $2.2B in 2021; however, from 2022 to 2024, consumption remained at a lower figure.

This repository contains the different outputs generated for the paper "Blockchain-enabled Server-less Federated Learning", submitted to Computer Networks (COMNET) journal. Two types of outputs are provided:

1. Blockchain queue simulator (output_queue_simulator): results of the simulations done in the batch-service queue simulator (https://github.com/fwilhelmi/batch_service_queue_simulator) to characterize the queue latency of blockchain applications.
2. Tensorflow (output_tensorflow): results of the simulations done in Tensorflow Federated (TFF), resulting from the application of different models to the federated EMNIST dataset.

Each folder also includes the scripts used to execute the corresponding simulations. For more details, see the repository in https://github.com/fwilhelmi/blockchain_enabled_federated_learning

Edge Federated Learning Market Outlook

As per our latest research, the global Edge Federated Learning market size in 2024 stands at USD 386 million, with a robust compound annual growth rate (CAGR) of 28.6% projected from 2025 to 2033. The market is anticipated to reach USD 3.68 billion by 2033, reflecting the surging demand for decentralized AI models and privacy-preserving machine learning solutions. The primary growth factor driving this expansion is the rapid proliferation of connected devices and the urgent need for secure, real-time data processing at the network edge.

One of the most significant growth factors for the Edge Federated Learning market is the exponential rise in data generated by IoT devices across various industries. Traditional centralized machine learning models are increasingly challenged by bandwidth limitations, latency issues, and data privacy concerns. Edge federated learning addresses these challenges by enabling collaborative model training on distributed edge devices, eliminating the need to transfer sensitive data to a central server. This approach not only enhances data privacy and security but also reduces network congestion, making it an attractive solution for sectors like healthcare, finance, and autonomous vehicles where data sensitivity and real-time insights are paramount.

Another critical driver is the growing regulatory landscape emphasizing data sovereignty and privacy. Governments and regulatory bodies worldwide are instituting stringent frameworks such as GDPR and HIPAA, compelling organizations to rethink their data management strategies. Edge federated learning aligns perfectly with these regulations by ensuring that personal and sensitive information remains localized on user devices while still contributing to the development of robust AI models. This regulatory push is especially evident in regions like Europe and North America, where compliance is non-negotiable, thus accelerating the adoption of federated learning at the edge.

The ongoing advancements in edge computing infrastructure and AI chipsets further bolster the growth of the Edge Federated Learning market. The integration of powerful edge hardware with optimized federated learning frameworks enables real-time analytics and decision-making capabilities at the device level. This synergy is fueling innovation in applications such as industrial IoT, smart cities, and autonomous transportation, where latency and reliability are crucial. Moreover, the increasing investments by technology giants and startups in edge AI platforms are creating a vibrant ecosystem that supports rapid experimentation and deployment of federated learning solutions.

From a regional perspective, North America currently leads the global market, driven by its advanced digital infrastructure, high adoption of IoT technologies, and strong presence of key industry players. Europe follows closely, backed by proactive regulatory frameworks and significant investments in privacy-preserving AI research. The Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, expanding 5G networks, and government initiatives to foster smart city development. Collectively, these regions are shaping the global trajectory of the Edge Federated Learning market, with unique adoption patterns and innovation hubs fueling the next wave of AI-powered edge applications.

Component Analysis

The Component segment of the Edge Federated Learning market is broadly categorized into hardware, software, and services. Hardware forms the backbone of edge federated learning deployments, encompassing edge servers, AI-optimized chipsets, and IoT gateways. The demand for specialized hardware is rising as organizations seek low-latency, high-performance solutions for on-device AI processing. Edge devices equipped with advanced GPUs, TPUs, and NPUs are increasingly being deployed to handle the computational demands of federated learning, particularly in resource-intensive applications like autonomous vehicles and industrial automation. This segment is witnessing rapid technological evolution, with hardware vendors collaborating with AI software providers to deliver integrated, scalable solutions tailored for federated learning.

Software solutions are equally critical, providing the frameworks and orchestration tools necessary for federated learning workflows. Leading technology companies are investing heavily in the de

The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.

This dataset provides mastodon toots that are using the hashtag FAIR-Data. This dataset is supposed to provide the foundation of further network analysis around the topic FAIR-Data.

Data Collection

Data was harvested using the Mastodon API. For each Mastodon server listed in the dataset, the API was employed to retrieve posts tagged with "fairdata". Up to 5,000 posts were collected per request, utilizing the API's pagination feature to obtain all available posts for that hashtag. Each Mastodon server was queried separately, and the results were stored in distinct CSV files.

Potential Duplicates

Given that Mastodon operates as a federated network, a post made on one instance can be replicated across different instances. This implies that the same post might appear in the data from multiple Mastodon servers, likely accounting for the duplicates observed in the dataset.

Analysis of Federation Dynamics: Duplicates can reveal which content is shared between servers and which servers are most active in the federation. In this context, duplicates could provide valuable insights for the network analysis.

The columns are:

1. id - A unique identifier for each post.
2. created_at - Timestamp indicating when the post was created.
3. content - The content or message of the post.
4. account - Detailed information about the account that created the post (ID, username, URL).
5. replies_count - Number of replies to the post.
6. reblogs_count - Number of reblogs or shares of the post.
7. favourites_count - Number of favorites or likes the post has received.
8. language - Language in which the post is written.
9. mentions - Any mentions in the post (for example, other users).
10. tags - Tags associated with the post.
11. emojis - Emojis used in the post.
12. category - Category of the post.

Federated Edge Learning Gateway Market Outlook

According to our latest research, the Federated Edge Learning Gateway market size reached a value of USD 1.82 billion in 2024, demonstrating robust momentum in the global digital transformation landscape. The market is forecasted to expand at a CAGR of 24.7% from 2025 to 2033, with the total market size projected to attain USD 15.15 billion by 2033. This remarkable growth trajectory is driven by the surging adoption of edge computing, the proliferation of Internet of Things (IoT) devices, and the increasing need for privacy-preserving, decentralized artificial intelligence (AI) models across various industries.

A significant growth factor for the Federated Edge Learning Gateway market is the urgent demand for data privacy and security, especially as enterprises manage sensitive information at the edge. With the rise of stringent data protection regulations such as GDPR and CCPA, organizations are seeking solutions that allow for distributed machine learning without transferring raw data to centralized servers. Federated edge learning gateways enable this by orchestrating the training of AI models locally while only sharing model updates, thereby minimizing privacy risks. This approach is particularly vital in sectors like healthcare and finance, where data confidentiality is paramount. The convergence of edge computing and federated learning is thus catalyzing the deployment of these gateways, further fueled by the exponential growth of connected devices and smart applications.

Another key driver bolstering market expansion is the rapid evolution of 5G networks and the increasing need for real-time data processing. As industries such as automotive, manufacturing, and smart cities integrate IoT and AI into their operations, the demand for low-latency, high-throughput solutions becomes critical. Federated edge learning gateways provide the necessary infrastructure to process and analyze data at the source, reducing latency and bandwidth consumption. This capability is crucial for mission-critical applications such as autonomous vehicles, predictive maintenance in industrial IoT, and personalized retail experiences. The synergy between 5G deployment and edge-based federated learning is expected to unlock new opportunities for innovation and operational efficiency, further accelerating market growth.

The expanding ecosystem of AI-powered applications across diverse verticals is also fueling the adoption of federated edge learning gateways. Enterprises are increasingly leveraging these solutions to enable collaborative learning across distributed endpoints, enhancing model accuracy and adaptability without compromising data sovereignty. In the BFSI sector, for instance, federated learning gateways facilitate fraud detection and risk assessment by aggregating insights from multiple branches while maintaining data locality. Similarly, in retail and energy & utilities, these gateways empower organizations to optimize inventory management, demand forecasting, and grid operations. The versatility and scalability of federated edge learning gateways are thus becoming indispensable for organizations striving to harness the full potential of AI at the edge.

From a regional perspective, North America currently dominates the Federated Edge Learning Gateway market owing to its advanced digital infrastructure, early adoption of edge AI technologies, and strong presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, expanding IoT deployments, and government initiatives promoting digital transformation. Europe, with its robust regulatory framework and focus on data privacy, is also witnessing significant traction. Latin America and the Middle East & Africa, while still nascent, are expected to register notable growth as enterprises in these regions increasingly recognize the value of federated edge learning in enhancing business agility and competitiveness.

Component Analysis

Workflow, topologies and computing plan classification adapted from Rieke et al. [1].

Federated Edge Computer Vision Market Outlook

According to our latest research, the global Federated Edge Computer Vision market size reached USD 1.82 billion in 2024, underpinned by the accelerating adoption of edge AI solutions across industries. The market is poised for robust expansion, projected to grow at a CAGR of 17.6% from 2025 to 2033, and is anticipated to achieve a value of USD 8.09 billion by 2033. This remarkable growth is driven primarily by the convergence of federated learning and edge computing, which is transforming how organizations process and analyze visual data in real time while preserving data privacy.

One of the primary growth factors propelling the Federated Edge Computer Vision market is the rising demand for real-time data processing and analytics at the edge. As industries such as surveillance, healthcare, automotive, and manufacturing increasingly deploy IoT devices and smart cameras, there is a growing need to analyze vast amounts of visual data locally. Federated edge computer vision enables organizations to harness AI-powered insights without transferring sensitive data to centralized cloud servers, thus reducing latency and bandwidth consumption. This approach not only enhances operational efficiency but also addresses stringent data privacy regulations, making it highly attractive for sectors dealing with confidential or regulated information.

Another significant driver is the surge in adoption of Industry 4.0 and smart infrastructure initiatives globally. As manufacturing plants, smart cities, and autonomous vehicle ecosystems become more interconnected, the ability to process and interpret visual information at the edge becomes critical. Federated learning empowers edge devices to collaboratively improve AI models without sharing raw data, which is especially valuable in environments where data sovereignty and low-latency decision-making are paramount. The proliferation of 5G networks further amplifies this trend by enabling faster data transfer and more reliable connectivity for distributed edge devices, thereby accelerating market growth.

Furthermore, the market is benefiting from continuous advancements in edge hardware and AI algorithms. Leading technology vendors are developing specialized chipsets and edge servers optimized for computer vision workloads, enabling more efficient inference and training on resource-constrained devices. The integration of federated learning frameworks with edge platforms is fostering innovation in domains such as predictive maintenance, intelligent video analytics, and remote healthcare monitoring. As enterprises recognize the value of decentralized AI and the potential for significant cost savings, investments in federated edge computer vision solutions are expected to surge throughout the forecast period.

Regionally, North America and Asia Pacific are at the forefront of adoption, with Europe closely following. North America’s leadership is attributed to early investments in AI research, robust IoT infrastructure, and a strong presence of technology giants. Meanwhile, Asia Pacific is witnessing exponential growth due to rapid urbanization, large-scale smart city projects, and government initiatives promoting digital transformation. Europe’s focus on regulatory compliance and data privacy is also driving demand for federated edge solutions, particularly in healthcare and automotive sectors. As regional governments and enterprises continue to prioritize data security and operational agility, the global Federated Edge Computer Vision market is set for sustained expansion.

Component Analysis

The Federated Edge Computer Vision market is segmented by component into hardware, software, and services, each playing a pivotal role in enabling robust and scalable edge AI solutions. The hardware segment encompasses edge servers, AI-optimized processors, and smart cameras, which are fundamental for executing complex computer vision algorithms locally. The evolution of hardware tailored for AI inference at the edge has significantly reduced the dependency on centralized data centers, allowing for faster and more energy-efficient processing. This is particularly crucial for applications requiring real-time responses, such as surveillance and autonomous vehicles, where latency can directly impact safety and performance. As the demand for high-performance, low-power edge devices continues to rise, the hardware segment is expected to maintain a substantial share of the overall market.

Federated Learning Solution Market Outlook

The global market size of federated learning solutions was valued at approximately USD 150 million in 2023 and is projected to reach USD 2.3 billion by 2032, growing at an impressive compound annual growth rate (CAGR) of 35.4% during the forecast period. This tremendous growth is driven by a surge in data privacy concerns, advancements in artificial intelligence, and increasing demand for decentralized machine learning solutions.

One of the primary growth factors for the federated learning solution market is the rising awareness of data security and privacy. With increasing incidents of data breaches and stringent regulations like GDPR and CCPA, organizations are compelled to adopt more secure data handling practices. Federated learning allows data to remain decentralized, enabling organizations to train machine learning models without transferring sensitive data to a central server, thereby significantly mitigating privacy risks.

Another driving factor is the rapid technological advancements in artificial intelligence and machine learning. The integration of AI into various business processes necessitates the use of advanced learning methods to improve operational efficiency and productivity. Federated learning offers a more efficient way to train AI models across multiple devices or locations without the need for centralized data storage, making it an attractive option for organizations seeking to harness the power of AI while maintaining data integrity.

Moreover, the growing volume of data generated from connected devices and the Internet of Things (IoT) is creating an enormous demand for advanced data analytics solutions. Federated learning provides a perfect fit for IoT environments where data is generated from numerous distributed sources. It allows for machine learning at the edge, reducing latency and bandwidth usage while ensuring that the data stays where it is generated.

From a regional perspective, North America is expected to hold the largest market share due to early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by increasing investments in AI and machine learning technologies, a growing number of tech startups, and rising awareness about data privacy issues.

Component Analysis

In the federated learning solution market, the component segment is divided into software and services. The software segment encompasses the platforms and tools required for deploying federated learning models. This includes frameworks, SDKs, and APIs that facilitate the integration of federated learning into existing systems. The increasing demand for customizable and scalable software solutions that can seamlessly integrate with various business operations is driving the growth of this segment.

The services segment includes consulting, implementation, training, and support services. As organizations increasingly adopt federated learning solutions, the need for expert guidance and support becomes critical. Consulting services help enterprises understand the benefits and implementation strategies for federated learning, while implementation services ensure the successful deployment of these solutions. Training services are essential for upskilling the workforce to effectively utilize federated learning technologies.

In terms of market share, the software segment is expected to dominate due to the continuous development and enhancement of federated learning platforms. Companies are investing heavily in research and development to introduce advanced features and capabilities, such as improved model accuracy, enhanced security protocols, and better scalability. This focus on innovation is likely to drive the adoption of federated learning software solutions across various industries.

Meanwhile, the services segment is projected to grow at a significant rate, fueled by the increasing complexity of federated learning deployments and the need for specialized services. As organizations aim to maximize the benefits of federated learning, the demand for professional services that offer end-to-end support—from planning and implementation to maintenance and optimization—is expected to rise.

Overall, the component analysis reveals that both software and services play crucial roles in the federated learning solution market. While software solutions form the backbone of federated learning implementatio

Federated Learning Edge Box Market Outlook

According to our latest research, the Federated Learning Edge Box market size reached USD 1.32 billion in 2024 at a robust year-on-year growth, driven by the increasing adoption of edge AI solutions and privacy-centric data processing. The market is set to expand at a CAGR of 28.6% during the forecast period, with the projected market size expected to reach USD 11.43 billion by 2033. This remarkable growth is fueled by the convergence of edge computing, artificial intelligence, and the rising need for decentralized machine learning frameworks that protect data privacy and minimize latency.

The primary growth driver for the Federated Learning Edge Box market is the global surge in demand for privacy-preserving AI and machine learning solutions. As organizations face stringent data protection regulations such as GDPR and CCPA, the ability to process and train models on distributed data without transferring sensitive information to a central server has become paramount. Federated learning edge boxes provide a secure and efficient platform for decentralized AI, enabling industries like healthcare, finance, and automotive to leverage real-time insights while maintaining compliance and trust. The proliferation of IoT devices and the exponential growth in data generated at the edge further amplify the need for such solutions, as they offer low-latency processing and reduce the risk of data breaches.

Another significant factor contributing to the market’s expansion is the rapid advancement in edge hardware and software technologies. Innovations in AI accelerators, embedded systems, and lightweight machine learning frameworks have made it feasible to deploy federated learning models directly on edge devices. This technological evolution allows organizations to harness the collective intelligence of distributed data sources, improving model accuracy and operational efficiency. Additionally, the increasing collaboration between technology vendors, research institutions, and industry consortia is fostering innovation and standardization in federated learning protocols, further accelerating market adoption across various sectors.

The integration of federated learning edge boxes with emerging technologies such as 5G, blockchain, and secure multi-party computation is also playing a crucial role in market growth. These integrations enhance data security, enable seamless communication between distributed nodes, and facilitate real-time analytics for mission-critical applications. For instance, in the healthcare sector, federated learning edge boxes are being used to train diagnostic models on patient data from multiple hospitals without compromising patient privacy. Similarly, in the automotive industry, they enable collaborative learning across vehicles to improve autonomous driving algorithms. As these use cases mature and scale, the demand for federated learning edge boxes is expected to rise exponentially.

From a regional perspective, North America currently leads the Federated Learning Edge Box market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the strong presence of leading AI and edge computing companies, significant investments in research and development, and early adoption of privacy-centric technologies. Europe’s growth is bolstered by strict data protection regulations and a robust industrial base, while Asia Pacific is witnessing rapid adoption due to the proliferation of IoT devices and smart infrastructure projects. As the market continues to evolve, emerging economies in Latin America and the Middle East & Africa are also expected to witness accelerated growth, driven by digital transformation initiatives and increasing awareness of data privacy.

Component Analysis

The Component segment of the Federated Learning Edge Box market is divided into Hardware, Software, and Services, each playing a pivotal role in the ecosystem. Hardware forms the backbone of edge computing, encompassing AI-optimized processors, embedded systems, and secure storage modules. The rapid evolution of edge hardware, such as AI accelerators and custom chipsets, has significantly enhanced the computational capabilities of federated learning edge boxes, enabling them to process complex machine learning tasks with minimal latency and power consumption. This advancement is part

SWDC 3Waters Asset Data exported out of Master database daily at Wellington Water Ltd (WWL). Height levels are in terms of NZVD2016 as of 1 July 2022.*This Federated Feature Service references our data in an Enterprise geodatabase (egdb) which is updated daily via FME workbench from InfoAsset. It has limited symbology and includes Abandoned, removed & virtual assets. It's purpose is so that staff can access the raw data which is updated daily. They can add it to their Ar Pro projects and it allows them to view the attribute tables & change symbols. It's other purpose is so that councils and the public can download the data form our Open Data Portal. It is only shared with our organisation in Enterprise because we dont want the public to use it as it is hosted on "our" server which is not as robust as the ESRI server. Also, if it's shared with the public, it may slow down the service for our staff. So every week it is copied to AGOL as a Hosted Feature Service which is shared with the public and our Open Data Portal.

Data composition and characteristics of included studies.

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Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.