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市場調查報告書
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1600819

聯邦政府學習解決方案市場：聯邦政府學習類型、產業、應用 - 2025-2030 年全球預測

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個工作天內

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2023年聯邦政府學習解決方案市值為1.4455億美元，預計到2024年將達到1.6634億美元，複合年成長率為15.22%，預計到2030年將達到3.8974億美元。

聯邦學習 (FL) 解決方案包含分散式機器學習方法，其中資料保留在本地，允許模型在不集中資料的情況下進行集體訓練。由於 GDPR 等資料隱私法規以及與資料傳輸相關的高成本，這種去中心化方法至關重要。 FL 在安全分析敏感患者資料的醫療保健領域、用於詐騙偵測的金融領域以及設備不斷產生資料的物聯網應用中越來越受歡迎。其範圍擴展到任何重視資料隱私和計算資源高效利用的行業。市場成長是由日益成長的資料隱私問題和對可擴展機器學習模型的需求所推動的。互聯設備的普及正在推動智慧家庭、自動駕駛汽車和個人化廣告等領域的需求並提供機會。硬體安全模組和安全多方運算的技術進步為創新提供了途徑。

主要市場統計
基準年[2023] 14455萬美元
預測年份 [2024] 16634萬美元
預測年份 [2030] 3.8974 億美元
複合年成長率（%） 15.22%

關鍵的成長要素包括機器學習演算法的增強，這些演算法可以提高模型聚合的準確性以及跨不同資料集和裝置的互通性。然而，高通訊成本和維護同步本地模型的複雜性等限制構成了主要障礙，特別是在資源有限的環境中。安全挑戰，包括潛在的對抗性攻擊，也限制了採用。為了利用 FL，相關人員應該投資邊緣運算基礎設施，探索與雲端服務供應商的夥伴關係，並強調隱私保護技術和強大的安全措施以增加客戶信任。

創新領域包括開發輕量級加密解決方案、開發更有效率的協作平均演算法以及解決資料和裝置功能的異質性。鼓勵隱私量化框架和自適應通訊協定的研究可以解決不同的資料分佈和設備功率限制。市場正在不斷發展，重點關注解決方案的模組化和互通性，為將聯合學習與現有數位轉型策略相結合的協作平台提供了空間。總體而言，雖然存在相當大的挑戰，但對資料隱私的日益關注和設備的激增為公司在這個不斷擴大的領域進行創新和獲取價值提供了重大機會。

市場動態：針對快速發展的聯邦學習解決方案市場揭示的關鍵市場洞察

聯邦學習解決方案市場正在因供需的動態交互作用而轉變。透過了解這些不斷變化的市場動態，公司可以準備好做出明智的投資決策、完善策略決策並抓住新的商機。全面了解這些趨勢可以幫助企業降低政治、地理、技術、社會和經濟領域的風險，同時消費行為及其對製造成本的影響以及對採購趨勢的影響。

  • 市場促進因素
    • 設備和組織之間的學習需求不斷成長
    • 由於機器學習的進步，IOFT 受到越來越多的關注
    • 能夠在去中心化設備上訓練演算法，以確保更好的資料隱私和安全性
  • 市場限制因素
    • 缺乏熟練的技術專家
  • 市場機會
    • 組織可以透過在設備上儲存資料來利用共用機器學習模型
    • 能夠在智慧型裝置上啟用預測功能，​​而不影響使用者體驗和隱私
  • 市場挑戰
    • 高延遲和通訊效率低下的問題

波特五力：駕馭聯邦學習解決方案市場的策略工具

波特的五力架構是了解聯邦政府學習解決方案市場競爭格局的關鍵工具。波特的五力框架為評估公司的競爭地位和探索策略機會提供了清晰的方法。該框架可幫助公司評估市場動態並確定新業務的盈利。這些見解使公司能夠利用自己的優勢，解決弱點並避免潛在的挑戰，從而確保更強大的市場地位。

PESTLE分析：了解聯邦政府學習解決方案市場的外部影響

外部宏觀環境因素在塑造聯邦政府學習解決方案市場的績效動態方面發揮著至關重要的作用。對政治、經濟、社會、技術、法律和環境因素的分析提供了應對這些影響所需的資訊。透過調查 PESTLE 因素，公司可以更了解潛在的風險和機會。這種分析可以幫助公司預測法規、消費者偏好和經濟趨勢的變化，並為他們做出積極主動的決策做好準備。

市場佔有率分析了解聯邦政府學習解決方案市場的競爭格局

對聯邦政府學習解決方案市場的詳細市場佔有率分析可以對供應商績效進行全面評估。公司可以透過比較收益、客戶群和成長率等關鍵指標來揭示其競爭地位。該分析揭示了市場集中、分散和整合的趨勢，為供應商提供了製定策略決策所需的洞察力，使他們能夠在日益激烈的競爭中佔有一席之地。

FPNV 聯邦政府學習解決方案市場供應商定位矩陣績效評估

FPNV 定位矩陣是評估聯邦政府學習解決方案市場供應商的關鍵工具。此矩陣允許業務組織根據商務策略和產品滿意度評估供應商，從而做出與其目標相符的明智決策。四個象限清楚且準確地分類供應商，幫助使用者辨識最能滿足其策略目標的合作夥伴和解決方案。

本報告提供了涵蓋關鍵重點領域的全面市場分析：

1. 市場滲透率：對當前市場環境的詳細回顧，包括行業主要企業的大量資料。

2. 市場開拓：辨識新興市場的成長機會，評估現有領域的擴張潛力，並提供未來成長的策略藍圖。

3. 市場多元化：分析近期產品發布、開拓地區、關鍵產業進展、塑造市場的策略投資。

4. 競爭評估與情報：徹底分析競爭格局，檢驗市場佔有率、業務策略、產品系列、認證、監管核准、專利趨勢、主要企業的技術進步等。

5. 產品開發與創新：重點在於有望推動未來市場成長的最尖端科技、研發活動和產品創新。

我們也回答重要問題，以幫助相關人員做出明智的決策：

1.目前的市場規模和未來的成長預測是多少？

2. 哪些產品、區隔市場和地區提供最佳投資機會？

3.塑造市場的主要技術趨勢和監管影響是什麼？

4.主要廠商的市場佔有率和競爭地位如何？

5. 推動供應商市場進入和退出策略的收益來源和策略機會是什麼？

目錄

第1章 前言

第2章調查方法

第3章執行摘要

第4章市場概況

第5章市場洞察

  • 市場動態
    • 促進因素
      • 設備和組織之間的學習需求不斷成長
      • 機器學習的進步推動了對工業物聯網的更多關注
      • 透過在去中心化設備上訓練演算法來提高資料隱私和安全性的能力
    • 抑制因素
      • 缺乏熟練的技術專長
    • 機會
      • 組織透過在設備上儲存資料來利用共用機器學習模型的潛力
      • 能夠在智慧型裝置上啟用預測功能，​​而不影響使用者體驗和隱私
    • 任務
      • 高延遲和通訊效率低下的問題
  • 市場區隔分析
    • 類型：在保護資料隱私的同時訓練機器學習模型的技術
    • 行業：根據不同行業對聯邦學習解決方案的需求優先考慮
    • 應用：聯邦學習解決方案在廣泛應用中的重要性
  • 波特五力分析
  • PESTEL分析
    • 政治的
    • 經濟
    • 社群
    • 技術的
    • 合法地
    • 環境
  • 客戶客製化

第6章以聯邦學習類型分類的聯邦學習解決方案市場

  • 集中
  • 去中心化
  • 外星人

第7章聯邦學習解決方案市場：按行業

  • 銀行、金融服務和保險
  • 能源/公共產業
  • 醫療保健和生命科學
  • 製造業
  • 零售/電子商務

第8章聯邦政府學習解決方案市場：按應用分類

  • 資料隱私和安全管理
  • 藥物發現
  • 工業物聯網
  • 線上視覺目標檢測
  • 風險管理
  • 個人化的購物體驗

第9章美洲聯邦學習解決方案市場

  • 阿根廷
  • 巴西
  • 加拿大
  • 墨西哥
  • 美國

第10章亞太聯邦政府學習解決方案市場

  • 澳洲
  • 中國
  • 印度
  • 印尼
  • 日本
  • 馬來西亞
  • 菲律賓
  • 新加坡
  • 韓國
  • 台灣
  • 泰國
  • 越南

第11章歐洲、中東和非洲的聯邦政府學習解決方案市場

  • 丹麥
  • 埃及
  • 芬蘭
  • 法國
  • 德國
  • 以色列
  • 義大利
  • 荷蘭
  • 奈及利亞
  • 挪威
  • 波蘭
  • 卡達
  • 俄羅斯
  • 沙烏地阿拉伯
  • 南非
  • 西班牙
  • 瑞典
  • 瑞士
  • 土耳其
  • 阿拉伯聯合大公國
  • 英國

第12章競爭格局

  • 2023 年市場佔有率分析
  • FPNV 定位矩陣，2023
  • 競爭情境分析
    • Consilient 向市場推出金融犯罪檢測的下一代聯合學習解決方案
    • FedML 宣布與 Theta Network 合作，為生成人工智慧和廣告推薦提供聯合機器學習
    • EIC 向 Ekkono Solutions津貼250 萬歐元資金用於開發聯邦學習軟體

公司名單

  • 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