Press release
The Global Federated Learning Solutions Market Reach USD 301.9 Million by 2032, Growing with CAGR of 10.2%
Federated learning is a machine learning method that has an algorithm across several decentralized edge devices or servers carrying local data samples. This method is in contrast with the traditional centralized machine learning techniques where all the local datasets are stored on a single server. In addition, this technique makes sure that the local data samples are identically dispersed in the server.
๐๐๐ช๐ฎ๐๐ฌ๐ญ ๐๐๐ฆ๐ฉ๐ฅ๐ ๐๐๐ฉ๐จ๐ซ๐ญ (๐๐๐ญ ๐ ๐ฎ๐ฅ๐ฅ ๐๐ง๐ฌ๐ข๐ ๐ก๐ญ๐ฌ ๐ข๐ง ๐๐๐ - 350 ๐๐๐ ๐๐ฌ) ๐๐ญ: https://www.alliedmarketresearch.com/request-sample/A14591
The global federated learning solutions market size was valued at $125.9 million in 2023, and is projected to reach $301.9 million by 2032, growing at a CAGR of 10.2% from 2024 to 2032.
The federated learning solutions market is segmented into component, application, industry vertical, and region. By component, it is bifurcated into solution and services. By application, it is divided into drug discovery, data privacy & security management, risk management, shopping experience personalization, industrial internet of things, online visual object detection, and others. By industry vertical, it is classified into BFSI, healthcare & life sciences, retail & e-commerce, manufacturing, energy & utilities, and others. Region-wise, the market is analyzed across North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa.
๐๐๐๐๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐ฎ๐ฅ๐ฅ ๐ฌ๐ฎ๐ฆ๐ฆ๐๐ซ๐ฒ ๐๐ญ : https://www.alliedmarketresearch.com/federated-learning-solutions-market-A14591
๐๐๐๐๐ง๐ญ ๐๐๐ฒ ๐๐ญ๐ซ๐๐ญ๐๐ ๐ข๐๐ฌ ๐๐ง๐ ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐ฆ๐๐ง๐ญ๐ฌ
In July 2023, FedML raised $11.5 million in funding to expand development and adoption for its distributed MLOps platform, which helps companies efficiently train and serve custom generative AI and large language models using proprietary data, while reducing costs through decentralized GPU cloud resources shared by the community.
In July 2021, HUB Security Ltd. partnered with Scaleout. The partnership is expected to focus on applications where the data for AI is distributed across multiple silos and performance requires physical proximity to the data sources.
๐๐ ๐ฒ๐จ๐ฎ ๐ก๐๐ฏ๐ ๐๐ง๐ฒ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ, ๐๐ฅ๐๐๐ฌ๐ ๐๐๐๐ฅ ๐๐ซ๐๐ ๐ญ๐จ ๐๐จ๐ง๐ญ๐๐๐ญ ๐จ๐ฎ๐ซ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐ญ: https://www.alliedmarketresearch.com/connect-to-analyst/A14591
๐๐ง๐๐ฎ๐ฌ๐ญ๐ซ๐ฒ ๐๐ซ๐๐ง๐๐ฌ
In June 2022, the U.S. and UK Governments collaborated to drive the Privacy-enhancing technologies (PETs) that could play a transformative role in addressing these financial crimes. PETs include maturing technologies, such as federated learning, which allows machine learning models to be trained on high-quality datasets collaboratively among organizations, without the data leaving safe environments.
๐๐จ๐ฆ๐ฉ๐๐ญ๐ข๐ญ๐ข๐ฏ๐ ๐๐๐ง๐๐ฌ๐๐๐ฉ๐
The major players operating in the federated learning solutions market share include Cloudera Inc, Intellegens Ltd., DataFleets Ltd, Nvidia Corporation, Owkin Inc., International Business Machines Corporation, Edge Delta Inc., Enveil Inc., Microsoft Corporation, and Alphabet Inc.
๐๐ง๐ช๐ฎ๐ข๐ซ๐ฒ ๐๐๐๐จ๐ซ๐ ๐๐ฎ๐ฒ๐ข๐ง๐ : https://www.alliedmarketresearch.com/purchase-enquiry/A14591
๐๐๐ฒ ๐๐๐ค๐๐๐ฐ๐๐ฒ๐ฌ
The federated learning solutions industry study covers 20 countries. The research includes a segment analysis of each country in terms of value ($Million) for the projected period 2024-2032.
More than 1,500 product literature, industry releases, annual reports, and other such documents of major federated learning solutions industry participants along with authentic industry journals, trade associations' releases, and government websites have been reviewed for generating high-value industry insights.
The study integrated high-quality data, professional opinions and analysis, and critical independent perspectives. The research approach is intended to provide a balanced view of global markets and assist stakeholders in making educated decisions to achieve their most ambitious growth objectives.
๐๐ฎ๐ฒ ๐๐จ๐ฐ & ๐๐๐ญ ๐๐ฉ๐ญ๐จ ๐๐% ๐๐ข๐ฌ๐๐จ๐ฎ๐ง๐ญ ๐จ๐ง ๐ญ๐ก๐ข๐ฌ ๐๐๐ฉ๐จ๐ซ๐ญ (350 ๐๐๐ ๐๐ฌ ๐๐๐ ๐ฐ๐ข๐ญ๐ก ๐๐ง๐ฌ๐ข๐ ๐ก๐ญ๐ฌ, ๐๐ก๐๐ซ๐ญ๐ฌ, ๐๐๐๐ฅ๐๐ฌ, ๐๐ง๐ ๐ ๐ข๐ ๐ฎ๐ซ๐๐ฌ) ๐๐ญ: https://www.alliedmarketresearch.com/federated-learning-solutions-market/purchase-options
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๐๐๐จ๐ฎ๐ญ ๐๐ฌ:
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