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 June of 2025.

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:

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.

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

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.

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 June 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.

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

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.

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

Data composition and characteristics of included studies.

FedE4RAG_Dataset

This is the dataset of the paper Privacy-Preserving Federal Embedding Learning for Localized Retrieval-Augmented Generation. FedE4RAG addresses data scarcity and privacy challenges in private RAG systems. It uses federated learning (FL) to collaboratively train client-side RAG retrieval models, keeping raw data localized. The framework employs knowledge distillation for effective server-client communication and homomorphic encryption to enhance parameter privacy.… See the full description on the dataset page: https://huggingface.co/datasets/DocAILab/FedE4RAG_Dataset.

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.

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.

Combination of RFE embedding model and federated algorithm.

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.

PCC 3Waters Asset Data exported from the Master Database at Wellington Water Ltd. It is made up of 19 layers. Please check the date for the latest Data Updated date.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 Arc 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.

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.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

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 night it is copied to AGOL as a Hosted Feature Service which is shared with the public and our Open Data Portal. The "copying" happens via a Distributed Collaboration (Chrissy S)

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.