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市场调查报告书
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联邦政府学习解决方案市场：联邦政府学习类型、产业、应用 - 2025-2030 年全球预测

Federated Learning Solutions Market by Federal Learning Types (Centralized, Decentralized, Heterogeneous), Vertical (Banking, Financial Services, & Insurance, Energy & Utilities, Healthcare & Life Sciences), Application - Global Forecast 2025-2030
出版日期: | 出版商: 360iResearch | 英文 194 Pages | 商品交期: 最快1-2个工作天内

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2023年联邦政府学习解决方案市值为1.4455亿美元，预计到2024年将达到1.6634亿美元，复合年增长率为15.22%，预计到2030年将达到3.8974亿美元。

联邦学习 (FL) 解决方案包含分散式机器学习方法，其中资料保留在本地，允许模型在不集中资料的情况下进行集体训练。由于 GDPR 等资料隐私法规以及与资料传输相关的高成本，这种去中心化方法至关重要。 FL 在安全分析敏感患者资料的医疗保健领域、用于诈骗侦测的金融领域以及设备不断产生资料的物联网应用中越来越受欢迎。其范围扩展到任何重视资料隐私和计算资源高效利用的行业。市场成长是由日益增长的资料隐私问题和对可扩展机器学习模型的需求所推动的。互联设备的普及正在推动智慧家庭、自动驾驶汽车和个人化广告等领域的需求并提供机会。硬体安全模组和安全多方运算的技术进步为创新提供了途径。

主要市场统计
基准年[2023] 14455万美元
预测年份 [2024] 16634万美元
预测年份 [2030] 3.8974 亿美元
复合年增长率（%） 15.22%

关键的成长要素包括机器学习演算法的增强，这些演算法可以提高模型聚合的准确性以及跨不同资料集和装置的互通性。然而，高通讯成本和维护同步本地模型的复杂性等限制构成了主要障碍，特别是在资源有限的环境中。安全挑战，包括潜在的对抗性攻击，也限制了采用。为了利用 FL，相关人员应该投资边缘运算基础设施，探索与云端服务供应商的伙伴关係，并强调隐私保护技术和强大的安全措施以增加客户信任。

创新领域包括开发轻量级加密解决方案、开发更有效率的协作平均演算法以及解决资料和装置功能的异质性。鼓励隐私量化框架和自适应通讯协定的研究可以解决不同的资料分布和设备功率限制。市场正在不断发展，重点关註解决方案的模组化和互通性，为将联合学习与现有数位转型策略相结合的协作平台提供了空间。总体而言，虽然存在相当大的挑战，但对资料隐私的日益关注和设备的激增为公司在这个不断扩大的领域进行创新和获取价值提供了重大机会。

市场动态：针对快速发展的联邦学习解决方案市场揭示的关键市场洞察

联邦学习解决方案市场正在因供需的动态交互作用而转变。透过了解这些不断变化的市场动态，公司可以准备好做出明智的投资决策、完善策略决策并抓住新的商机。全面了解这些趋势可以帮助企业降低政治、地理、技术、社会和经济领域的风险，同时消费行为及其对製造成本的影响以及对采购趋势的影响。

  • 市场驱动因素
    • 设备和组织之间的学习需求不断增长
    • 由于机器学习的进步，IOFT 受到越来越多的关注
    • 能够在去中心化设备上训练演算法，以确保更好的资料隐私和安全性
  • 市场限制因素
    • 缺乏熟练的技术专家
  • 市场机会
    • 组织可以透过在设备上储存资料来利用共用机器学习模型
    • 能够在智慧型装置上启用预测功能，​​而不影响使用者体验和隐私
  • 市场挑战
    • 高延迟和通讯效率低下的问题

波特五力：驾驭联邦学习解决方案市场的策略工具

波特的五力架构是了解联邦政府学习解决方案市场竞争格局的关键工具。波特的五力框架为评估公司的竞争地位和探索策略机会提供了清晰的方法。该框架可帮助公司评估市场动态并确定新业务的盈利。这些见解使公司能够利用自己的优势，解决弱点并避免潜在的挑战，从而确保更强大的市场地位。

PESTLE分析：了解联邦政府学习解决方案市场的外部影响

外部宏观环境因素在塑造联邦政府学习解决方案市场的绩效动态方面发挥着至关重要的作用。对政治、经济、社会、技术、法律和环境因素的分析提供了应对这些影响所需的资讯。透过调查 PESTLE 因素，公司可以更了解潜在的风险和机会。这种分析可以帮助公司预测法规、消费者偏好和经济趋势的变化，并为他们做出积极主动的决策做好准备。

市场占有率分析了解联邦政府学习解决方案市场的竞争格局

对联邦政府学习解决方案市场的详细市场占有率分析可以对供应商绩效进行全面评估。公司可以透过比较收益、客户群和成长率等关键指标来揭示其竞争地位。该分析揭示了市场集中、分散和整合的趋势，为供应商提供了製定策略决策所需的洞察力，使他们能够在日益激烈的竞争中占有一席之地。

FPNV 联邦政府学习解决方案市场供应商定位矩阵绩效评估

FPNV 定位矩阵是评估联邦政府学习解决方案市场供应商的关键工具。此矩阵允许业务组织根据商务策略和产品满意度评估供应商，从而做出与其目标相符的明智决策。四个象限清楚且准确地划分供应商，帮助使用者辨识最能满足其策略目标的合作伙伴和解决方案。

本报告提供了涵盖关键重点领域的全面市场分析：

1. 市场渗透率：对当前市场环境的详细回顾，包括行业主要企业的大量资料。

2. 市场开拓：辨识新兴市场的成长机会，评估现有领域的扩张潜力，并提供未来成长的策略蓝图。

3. 市场多元化：分析近期产品发布、开拓地区、关键产业进展、塑造市场的策略投资。

4. 竞争评估与情报：彻底分析竞争格局，检验市场占有率、业务策略、产品系列、认证、监管核准、专利趋势、主要企业的技术进步等。

5. 产品开发与创新：重点在于有望推动未来市场成长的最尖端科技、研发活动和产品创新。

我们也回答重要问题，以帮助相关人员做出明智的决策：

1.目前的市场规模和未来的成长预测是多少？

2. 哪些产品、区隔市场和地区提供最佳投资机会？

3.塑造市场的主要技术趋势和监管影响是什么？

4.主要厂商的市场占有率和竞争地位如何？

5. 推动供应商市场进入和退出策略的收益来源和策略机会是什么？

目录

第一章 前言

第二章调查方法

第三章执行摘要

第四章市场概况

第五章市场洞察

  • 市场动态
    • 促进因素
      • 设备和组织之间的学习需求不断增长
      • 机器学习的进步推动了对工业物联网的更多关注
      • 透过在去中心化设备上训练演算法来提高资料隐私和安全性的能力
    • 抑制因素
      • 缺乏熟练的技术专长
    • 机会
      • 组织透过在设备上储存资料来利用共用机器学习模型的潜力
      • 能够在智慧型装置上启用预测功能，​​而不影响使用者体验和隐私
    • 任务
      • 高延迟和通讯效率低下的问题
  • 市场区隔分析
    • 类型：在保护资料隐私的同时训练机器学习模型的技术
    • 行业：根据不同行业对联邦学习解决方案的需求优先考虑
    • 应用：联邦学习解决方案在广泛应用中的重要性
  • 波特五力分析
  • PESTEL分析
    • 政治的
    • 经济
    • 社群
    • 技术的
    • 合法地
    • 环境
  • 客户客製化

第六章以联邦学习类型分類的联邦学习解决方案市场

  • 集中
  • 去中心化
  • 外星人

第七章联邦学习解决方案市场：按行业

  • 银行、金融服务和保险
  • 能源/公共产业
  • 医疗保健和生命科学
  • 製造业
  • 零售/电子商务

第八章联邦政府学习解决方案市场：按应用分类

  • 资料隐私和安全管理
  • 药物发现
  • 工业物联网
  • 线上视觉目标检测
  • 风险管理
  • 个人化的购物体验

第九章美洲联邦学习解决方案市场

  • 阿根廷
  • 巴西
  • 加拿大
  • 墨西哥
  • 美国

第十章亚太联邦政府学习解决方案市场

  • 澳洲
  • 中国
  • 印度
  • 印尼
  • 日本
  • 马来西亚
  • 菲律宾
  • 新加坡
  • 韩国
  • 台湾
  • 泰国
  • 越南

第十一章欧洲、中东和非洲的联邦政府学习解决方案市场

  • 丹麦
  • 埃及
  • 芬兰
  • 法国
  • 德国
  • 以色列
  • 义大利
  • 荷兰
  • 奈及利亚
  • 挪威
  • 波兰
  • 卡达
  • 俄罗斯
  • 沙乌地阿拉伯
  • 南非
  • 西班牙
  • 瑞典
  • 瑞士
  • 土耳其
  • 阿拉伯聯合大公国
  • 英国

第十二章竞争格局

  • 2023 年市场占有率分析
  • FPNV 定位矩阵，2023
  • 竞争情境分析
    • Consilient 向市场推出金融犯罪检测的下一代联合学习解决方案
    • FedML 宣布与 Theta Network 合作，为生成人工智慧和广告推荐提供联合机器学习
    • EIC 向 Ekkono Solutions津贴250 万欧元资金用于开发联邦学习软体

公司名单

  • Acuratio Inc.
  • apheris AI GmbH
  • Aptima, Inc.
  • BranchKey BV
  • Cloudera, Inc.
  • Consilient
  • Duality Technologies Inc.
  • Edge Delta, Inc.
  • Ekkono Solutions AB
  • Enveil, Inc.
  • Everest Global, Inc.
  • Faculty Science Limited
  • FedML
  • Google LLC by Alphabet Inc.
  • Hewlett Packard Enterprise Development LP
  • Integral and Open Systems, Inc.
  • Intel Corporation
  • Intellegens Limited
  • International Business Machines Corporation
  • Lifebit Biotech Ltd.
  • LiveRamp Holdings, Inc.
  • Microsoft Corporation
  • Nvidia Corporation
  • Oracle Corporation
  • Owkin Inc.
  • SAP SE
  • Secure AI Labs
  • Sherpa Europe SL
  • SoulPage IT Solutions
  • TripleBlind
  • WeBank Co., Ltd.
  • Zoho Corporation Pvt. Ltd.
Product Code: MRR-FD3F12D52B93

The Federated Learning Solutions Market was valued at USD 144.55 million in 2023, expected to reach USD 166.34 million in 2024, and is projected to grow at a CAGR of 15.22%, to USD 389.74 million by 2030.

Federated Learning (FL) Solutions encompasses a distributed machine learning approach where data remains local, enabling model training collectively without data centralization. This decentralized method is crucial due to data privacy regulations like GDPR and the high costs associated with data transfers. FL is gaining traction in healthcare for securely analyzing sensitive patient data, in the financial sector for fraud detection, and in IoT applications where devices continuously generate data. Its scope extends to any industry that values data privacy and efficient computational resource use. Market growth is driven by rising data privacy concerns and the need for scalable machine learning models. The increasing ubiquity of connected devices is amplifying demand, offering opportunities in sectors like smart homes, autonomous vehicles, and personalized advertising. Technological advancements in hardware security modules and secure multi-party computation offer avenues for innovation.

KEY MARKET STATISTICS
Base Year [2023] USD 144.55 million
Estimated Year [2024] USD 166.34 million
Forecast Year [2030] USD 389.74 million
CAGR (%) 15.22%

Key growth influencers include enhanced machine learning algorithms that improve model aggregation accuracy and interoperability between various datasets and devices. However, limitations such as high communication costs, especially in resource-constrained environments, and the complexity of maintaining synchronized local models pose significant hurdles. Security challenges, including potential adversarial attacks, also restrict widespread adoption. To capitalize on FL, stakeholders should invest in edge computing infrastructure and explore partnerships with cloud service providers, emphasizing privacy-preserving techniques and robust security measures to enhance customer trust.

Innovation areas include developing lightweight cryptographic solutions, more efficient federated averaging algorithms, and tackling heterogeneity in data and device capabilities. Encouraging research in privacy quantification frameworks and adaptive communication protocols can address varied data distributions and device power constraints. The market is evolving with a focus on solution modularity and interoperability, offering room for collaborative platforms that integrate federated learning with existing digital transformation strategies. Overall, while there are considerable challenges, the increasing emphasis on data privacy and the proliferation of devices present substantial opportunities for businesses to innovate and capture value within this expanding domain.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Federated Learning Solutions Market

The Federated Learning Solutions Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

  • Market Drivers
    • Increasing Need for Learning between Device & Organisation
    • Increasing Focus on IIOt with Advances in Machine Learning
    • Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices
  • Market Restraints
    • Lack of Skilled Technical Expertise
  • Market Opportunities
    • Organization's Potential to Leverage Shared ML Model by Storing Data on Device
    • Capability to Enable Predictive Features on Smart Devices without Impacting User Experience and Privacy
  • Market Challenges
    • Issue of High Latency and Communication Inefficiency

Porter's Five Forces: A Strategic Tool for Navigating the Federated Learning Solutions Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Federated Learning Solutions Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Federated Learning Solutions Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Federated Learning Solutions Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Federated Learning Solutions Market

A detailed market share analysis in the Federated Learning Solutions Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Federated Learning Solutions Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Federated Learning Solutions Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Key Company Profiles

The report delves into recent significant developments in the Federated Learning Solutions Market, highlighting leading vendors and their innovative profiles. These include Acuratio Inc., apheris AI GmbH, Aptima, Inc., BranchKey B.V., Cloudera, Inc., Consilient, Duality Technologies Inc., Edge Delta, Inc., Ekkono Solutions AB, Enveil, Inc., Everest Global, Inc., Faculty Science Limited, FedML, Google LLC by Alphabet Inc., Hewlett Packard Enterprise Development LP, Integral and Open Systems, Inc., Intel Corporation, Intellegens Limited, International Business Machines Corporation, Lifebit Biotech Ltd., LiveRamp Holdings, Inc., Microsoft Corporation, Nvidia Corporation, Oracle Corporation, Owkin Inc., SAP SE, Secure AI Labs, Sherpa Europe S.L., SoulPage IT Solutions, TripleBlind, WeBank Co., Ltd., and Zoho Corporation Pvt. Ltd..

Market Segmentation & Coverage

This research report categorizes the Federated Learning Solutions Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Based on Federal Learning Types, market is studied across Centralized, Decentralized, and Heterogeneous.
  • Based on Vertical, market is studied across Banking, Financial Services, & Insurance, Energy & Utilities, Healthcare & Life Sciences, Manufacturing, and Retail & e-Commerce.
  • Based on Application, market is studied across Data Privacy & Security Management, Drug Discovery, Industrial Internet of Things, Online Visual Object Detection, Risk Management, and Shopping Experience Personalization.
  • Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Segmentation & Coverage
  • 1.3. Years Considered for the Study
  • 1.4. Currency & Pricing
  • 1.5. Language
  • 1.6. Stakeholders

2. Research Methodology

  • 2.1. Define: Research Objective
  • 2.2. Determine: Research Design
  • 2.3. Prepare: Research Instrument
  • 2.4. Collect: Data Source
  • 2.5. Analyze: Data Interpretation
  • 2.6. Formulate: Data Verification
  • 2.7. Publish: Research Report
  • 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

  • 5.1. Market Dynamics
    • 5.1.1. Drivers
      • 5.1.1.1. Increasing Need for Learning between Device & Organisation
      • 5.1.1.2. Increasing Focus on IIOt with Advances in Machine Learning
      • 5.1.1.3. Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices
    • 5.1.2. Restraints
      • 5.1.2.1. Lack of Skilled Technical Expertise
    • 5.1.3. Opportunities
      • 5.1.3.1. Organization's Potential to Leverage Shared ML Model by Storing Data on Device
      • 5.1.3.2. Capability to Enable Predictive Features on Smart Devices without Impacting User Experience and Privacy
    • 5.1.4. Challenges
      • 5.1.4.1. Issue of High Latency and Communication Inefficiency
  • 5.2. Market Segmentation Analysis
    • 5.2.1. Types: Techniques for training machine learning models while preserving data privacy
    • 5.2.2. Vertical: Need-based preference for federated learning solutions across diverse industries
    • 5.2.3. Application: Significance of federated learning solutions for wide scope of applications
  • 5.3. Porter's Five Forces Analysis
    • 5.3.1. Threat of New Entrants
    • 5.3.2. Threat of Substitutes
    • 5.3.3. Bargaining Power of Customers
    • 5.3.4. Bargaining Power of Suppliers
    • 5.3.5. Industry Rivalry
  • 5.4. PESTLE Analysis
    • 5.4.1. Political
    • 5.4.2. Economic
    • 5.4.3. Social
    • 5.4.4. Technological
    • 5.4.5. Legal
    • 5.4.6. Environmental
  • 5.5. Client Customization

6. Federated Learning Solutions Market, by Federal Learning Types

  • 6.1. Introduction
  • 6.2. Centralized
  • 6.3. Decentralized
  • 6.4. Heterogeneous

7. Federated Learning Solutions Market, by Vertical

  • 7.1. Introduction
  • 7.2. Banking, Financial Services, & Insurance
  • 7.3. Energy & Utilities
  • 7.4. Healthcare & Life Sciences
  • 7.5. Manufacturing
  • 7.6. Retail & e-Commerce

8. Federated Learning Solutions Market, by Application

  • 8.1. Introduction
  • 8.2. Data Privacy & Security Management
  • 8.3. Drug Discovery
  • 8.4. Industrial Internet of Things
  • 8.5. Online Visual Object Detection
  • 8.6. Risk Management
  • 8.7. Shopping Experience Personalization

9. Americas Federated Learning Solutions Market

  • 9.1. Introduction
  • 9.2. Argentina
  • 9.3. Brazil
  • 9.4. Canada
  • 9.5. Mexico
  • 9.6. United States

10. Asia-Pacific Federated Learning Solutions Market

  • 10.1. Introduction
  • 10.2. Australia
  • 10.3. China
  • 10.4. India
  • 10.5. Indonesia
  • 10.6. Japan
  • 10.7. Malaysia
  • 10.8. Philippines
  • 10.9. Singapore
  • 10.10. South Korea
  • 10.11. Taiwan
  • 10.12. Thailand
  • 10.13. Vietnam

11. Europe, Middle East & Africa Federated Learning Solutions Market

  • 11.1. Introduction
  • 11.2. Denmark
  • 11.3. Egypt
  • 11.4. Finland
  • 11.5. France
  • 11.6. Germany
  • 11.7. Israel
  • 11.8. Italy
  • 11.9. Netherlands
  • 11.10. Nigeria
  • 11.11. Norway
  • 11.12. Poland
  • 11.13. Qatar
  • 11.14. Russia
  • 11.15. Saudi Arabia
  • 11.16. South Africa
  • 11.17. Spain
  • 11.18. Sweden
  • 11.19. Switzerland
  • 11.20. Turkey
  • 11.21. United Arab Emirates
  • 11.22. United Kingdom

12. Competitive Landscape

  • 12.1. Market Share Analysis, 2023
  • 12.2. FPNV Positioning Matrix, 2023
  • 12.3. Competitive Scenario Analysis
    • 12.3.1. Consilient Brings to Market its Next-Generation Federated Learning Solution for Financial Crime Detection
    • 12.3.2. FedML Announces Partnership with Theta Network to Empower Collaborative Machine Learning for Generative AI and Ad Recommendation
    • 12.3.3. EIC Grants Ekkono Solutions €2.5 Million in Funding for Federated Learning Software Development

Companies Mentioned

  • 1. Acuratio Inc.
  • 2. apheris AI GmbH
  • 3. Aptima, Inc.
  • 4. BranchKey B.V.
  • 5. Cloudera, Inc.
  • 6. Consilient
  • 7. Duality Technologies Inc.
  • 8. Edge Delta, Inc.
  • 9. Ekkono Solutions AB
  • 10. Enveil, Inc.
  • 11. Everest Global, Inc.
  • 12. Faculty Science Limited
  • 13. FedML
  • 14. Google LLC by Alphabet Inc.
  • 15. Hewlett Packard Enterprise Development LP
  • 16. Integral and Open Systems, Inc.
  • 17. Intel Corporation
  • 18. Intellegens Limited
  • 19. International Business Machines Corporation
  • 20. Lifebit Biotech Ltd.
  • 21. LiveRamp Holdings, Inc.
  • 22. Microsoft Corporation
  • 23. Nvidia Corporation
  • 24. Oracle Corporation
  • 25. Owkin Inc.
  • 26. SAP SE
  • 27. Secure AI Labs
  • 28. Sherpa Europe S.L.
  • 29. SoulPage IT Solutions
  • 30. TripleBlind
  • 31. WeBank Co., Ltd.
  • 32. Zoho Corporation Pvt. Ltd.

LIST OF FIGURES

  • FIGURE 1. FEDERATED LEARNING SOLUTIONS MARKET RESEARCH PROCESS
  • FIGURE 2. FEDERATED LEARNING SOLUTIONS MARKET SIZE, 2023 VS 2030
  • FIGURE 3. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, 2018-2030 (USD MILLION)
  • FIGURE 4. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 5. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 6. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2023 VS 2030 (%)
  • FIGURE 7. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 8. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2023 VS 2030 (%)
  • FIGURE 9. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 10. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2023 VS 2030 (%)
  • FIGURE 11. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 12. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 13. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 14. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY STATE, 2023 VS 2030 (%)
  • FIGURE 15. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 16. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 17. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 18. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 19. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 20. FEDERATED LEARNING SOLUTIONS MARKET SHARE, BY KEY PLAYER, 2023
  • FIGURE 21. FEDERATED LEARNING SOLUTIONS MARKET, FPNV POSITIONING MATRIX, 2023

LIST OF TABLES

  • TABLE 1. FEDERATED LEARNING SOLUTIONS MARKET SEGMENTATION & COVERAGE
  • TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2023
  • TABLE 3. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, 2018-2030 (USD MILLION)
  • TABLE 4. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 5. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 6. FEDERATED LEARNING SOLUTIONS MARKET DYNAMICS
  • TABLE 7. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 8. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY CENTRALIZED, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 9. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY DECENTRALIZED, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 10. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY HETEROGENEOUS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 11. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 12. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY BANKING, FINANCIAL SERVICES, & INSURANCE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 13. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY ENERGY & UTILITIES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 14. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY HEALTHCARE & LIFE SCIENCES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 15. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY MANUFACTURING, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 16. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY RETAIL & E-COMMERCE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 17. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 18. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY DATA PRIVACY & SECURITY MANAGEMENT, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 19. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY DRUG DISCOVERY, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 20. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY INDUSTRIAL INTERNET OF THINGS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 21. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY ONLINE VISUAL OBJECT DETECTION, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 22. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY RISK MANAGEMENT, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 23. GLOBAL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY SHOPPING EXPERIENCE PERSONALIZATION, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 24. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 25. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 26. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 27. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 28. ARGENTINA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 29. ARGENTINA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 30. ARGENTINA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 31. BRAZIL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 32. BRAZIL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 33. BRAZIL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 34. CANADA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 35. CANADA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 36. CANADA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 37. MEXICO FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 38. MEXICO FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 39. MEXICO FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 40. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 41. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 42. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 43. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
  • TABLE 44. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 45. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 46. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 47. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 48. AUSTRALIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 49. AUSTRALIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 50. AUSTRALIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 51. CHINA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 52. CHINA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 53. CHINA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 54. INDIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 55. INDIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 56. INDIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 57. INDONESIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 58. INDONESIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 59. INDONESIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 60. JAPAN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 61. JAPAN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 62. JAPAN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 63. MALAYSIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 64. MALAYSIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 65. MALAYSIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 66. PHILIPPINES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 67. PHILIPPINES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 68. PHILIPPINES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 69. SINGAPORE FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 70. SINGAPORE FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 71. SINGAPORE FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 72. SOUTH KOREA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 73. SOUTH KOREA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 74. SOUTH KOREA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 75. TAIWAN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 76. TAIWAN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 77. TAIWAN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 78. THAILAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 79. THAILAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 80. THAILAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 81. VIETNAM FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 82. VIETNAM FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 83. VIETNAM FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 84. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 85. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 86. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 87. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 88. DENMARK FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 89. DENMARK FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 90. DENMARK FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 91. EGYPT FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 92. EGYPT FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 93. EGYPT FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 94. FINLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 95. FINLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 96. FINLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 97. FRANCE FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 98. FRANCE FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 99. FRANCE FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 100. GERMANY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 101. GERMANY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 102. GERMANY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 103. ISRAEL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 104. ISRAEL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 105. ISRAEL FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 106. ITALY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 107. ITALY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 108. ITALY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 109. NETHERLANDS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 110. NETHERLANDS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 111. NETHERLANDS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 112. NIGERIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 113. NIGERIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 114. NIGERIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 115. NORWAY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 116. NORWAY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 117. NORWAY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 118. POLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 119. POLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 120. POLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 121. QATAR FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 122. QATAR FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 123. QATAR FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 124. RUSSIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 125. RUSSIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 126. RUSSIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 127. SAUDI ARABIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 128. SAUDI ARABIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 129. SAUDI ARABIA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 130. SOUTH AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 131. SOUTH AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 132. SOUTH AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 133. SPAIN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 134. SPAIN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 135. SPAIN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 136. SWEDEN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 137. SWEDEN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 138. SWEDEN FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 139. SWITZERLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 140. SWITZERLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 141. SWITZERLAND FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 142. TURKEY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 143. TURKEY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 144. TURKEY FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 145. UNITED ARAB EMIRATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 146. UNITED ARAB EMIRATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 147. UNITED ARAB EMIRATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 148. UNITED KINGDOM FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2018-2030 (USD MILLION)
  • TABLE 149. UNITED KINGDOM FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2018-2030 (USD MILLION)
  • TABLE 150. UNITED KINGDOM FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 151. FEDERATED LEARNING SOLUTIONS MARKET SHARE, BY KEY PLAYER, 2023
  • TABLE 152. FEDERATED LEARNING SOLUTIONS MARKET, FPNV POSITIONING MATRIX, 2023