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시장보고서
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헬스케어용 연합 학습 시장 규모, 점유율, 동향 분석 보고서 : 용도별, 배포 모드별, 최종 용도별, 지역별 부문 예측(2025-2030년)

Federated Learning In Healthcare Market Size, Share & Trends Analysis Report By Application, By Deployment Mode (On-premise, Cloud-based), By End-use, By Region, And Segment Forecasts, 2025 - 2030
발행일: | 리서치사: Grand View Research | 페이지 정보: 영문 200 Pages | 배송안내 : 2-10일 (영업일 기준)

    
    
    




※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문 목차를 참고해주시기 바랍니다.

시장 규모와 동향

세계의 헬스케어용 연합 학습 시장 규모는 2024년에 2,883만 달러에 달했으며 2025-2030년에 걸쳐 16.0%의 연평균 복합 성장률(CAGR)을 나타낼 것으로 예측되고 있습니다. 엔지니어링 통합은 안전하고 협력적인 AI 모델 개발을 위한 강력한 도구로서 건강 관리 부문에서 큰 관심을 끌고 있습니다. 데이터를 공유하지 않고도 AI 모델을 교육할 수 있으며, 개인정보 보호를 보장합니다.

연합 학습과 블록체인을 결합하여 의료기관은 AI 모델 개발을 위한 분산화된 안전한 인프라를 확립할 수 있습니다. 실험실이 가속화되어 환자의 기밀을 보호하면서 다양한 데이터세트에서 인사이트를 공유할 수 있게 해 줍니다.

헬스케어용 연합 학습은 여러 기관에 걸친 AI 모델을 교육하는 독특한 방법을 설명하고 있습니다. 이를 통해 AI 모델의 정확성을 높일 수 있습니다. 예를 들어 2024년 10월, 프레드 해치슨 암 센터, 다나퍼 바 암 실험실, 기념 슬론 케터링 암 센터, 시드니 킨멜 종합 암 센터, 아마존 웹서비스, 마이크로소프트, 엔비디아, 딜로이트 등 테크 대기업과의 협업에 의해 '암 AI 얼라이언스'가 설립되었습니다. 기밀성이 높은 환자 정보를 공유하지 않고 안전하고 분산화된 데이터 분산을 가능하게 하는 연합 학습을 통해 AI 주도의 암 의료를 추진합니다.

원격지에서는 연합 학습을 통해 건강 모니터링을 위한 웨어러블 및 스마트폰 등의 에지 디바이스에 직접 AI 모델을 배포할 수 있게 되어 있습니다. 같은 건강 지표를 디바이스에서 직접 실시간으로 분석할 수 있습니다. 성질환 관리와 충분한 서비스를 받지 않는 지역에서의 예방 헬스케어의 제공에 특히 유익합니다. 궁극적으로는 중앙 집중형의 인프라에의 의존을 줄이는 것과 동시에, AI를 활용한 헬스케어에의 접근성을 높일 수 있습니다.

건강 관리 기관은 환자 관리를 강화하기 위해 AI 구동 기술을 신속하게 도입하고 있습니다. 데이터를 로컬로 유지할 수 있는 연합 학습은 보안을 유지하면서 혁신을 촉진합니다. 예를 들어, 2024년 12월 독일의 의료 기술 기업인 지멘스 헬시니어스는 엔비디아와 협력하여 의료용 이미징 플랫폼에 MONAI Deploy를 통합했습니다.

목차

제1장 조사 방법과 범위

제2장 주요 요약

제3장 헬스케어용 연합 학습 시장 변수, 동향, 범위

  • 세계의 헬스케어용 연합 학습 시장 전망
  • 산업 밸류체인 분석
  • 시장 역학
    • 시장 성장 촉진요인 분석
    • 시장 성장 억제요인 분석
    • 산업의 과제
  • Porter's Five Forces 분석
    • 공급업체의 협상력
    • 구매자의 협상력
    • 대체 위협
    • 신규 참가로부터의 위협
    • 경쟁 기업간 경쟁 관계
  • PESTEL 분석
    • 정치
    • 경제
    • 사회
    • 기술
    • 환경
    • 법적

제4장 헬스케어용 연합 학습 시장 : 용도의 추정과 예측

  • 헬스케어용 연합 학습 시장 : 용도 변동 분석(2024년, 2030년)

제5장 헬스케어용 연합 학습 시장 : 배포 모드의 추정과 예측

  • 헬스케어용 연합 학습 시장 : 배포 모드 변동 분석(2024년, 2030년)

제6장 헬스케어용 연합 학습 시장 : 최종 용도의 전망의 추정과 예측

  • 헬스케어용 연합 학습 시장 : 최종 용도 변동 분석(2024년, 2030년)
    • 병원 및 의료기관
    • 제약 및 생명 공학 기업
    • 연구기관
    • 정부 및 규제기관

제7장 헬스케어용 연합 학습 시장 : 지역별, 추정·동향 분석

  • 헬스케어용 연합 학습 시장 점유율(지역별(2024년, 2030년), 100만 달러)
  • 북미
    • 미국
    • 캐나다
    • 멕시코
  • 유럽
    • 영국
    • 독일
    • 프랑스
  • 아시아태평양
    • 중국
    • 일본
    • 인도
    • 호주
    • 한국
  • 라틴아메리카
    • 브라질
  • 중동 및 아프리카
    • 아랍에미리트(UAE)
    • 사우디아라비아
    • 남아프리카

제8장 경쟁 구도

  • 주요 시장 진출기업에 의한 최근의 동향과 영향 분석
  • 공급업체 상황
    • 기업 분류
    • 주요 리셀러와 채널 파트너 목록
    • 잠재고객 일람
  • 경쟁 역학
    • 경쟁 벤치마킹
    • 전략 매핑
    • 히트맵 분석
  • 기업 프로파일/상장 기업
    • FedML
    • GE Healthcare
    • Google LLC
    • Health Catalyst
    • IBM Corporation
    • Medtronic
    • Microsoft
    • NVIDIA Corporation
    • Owkin, Inc.
    • Siemens Healthineers
KTH 25.05.30

Market Size & Trends:

The global federated learning in healthcare market size was estimated at USD 28.83 million in 2024 and is projected to grow at a CAGR of 16.0% from 2025 to 2030. The integration of federated learning with blockchain technology is gaining significant prominence in the healthcare sector as a powerful tool for secure and collaborative AI model development. Federated learning allows multiple healthcare institutions to train AI models on their data without directly sharing sensitive patient information, ensuring privacy is maintained. Blockchain technology adds another layer of security by providing an immutable ledger that tracks all interactions within the federated learning system. This ensures that data exchanges and model updates are transparent, auditable, and tamper-proof, which protects against unauthorized access or manipulation.

Combining federated learning with blockchain allows healthcare institutions to establish a decentralized and secure infrastructure for AI model development. Blockchain verifies and tracks model updates, increasing trust in the AI systems' outputs and decisions. This integration promotes greater collaboration across institutions, enabling the sharing of insights from diverse datasets while safeguarding patient confidentiality. Moreover, the combination of these technologies enhances the accountability of AI systems, making it easier to trace and audit model training and data handling processes.

In healthcare, federated learning offers a unique method for training AI models across multiple institutions. This approach enables each institution to keep its data secure and private without sharing sensitive patient information. The model is trained locally at each institution, and only model updates are shared, not the actual data. Collaborating in this way allows institutions to pool their expertise and data diversity, which in turn improves the accuracy of AI models. Ultimately, federated learning provides a way to enhance healthcare solutions while maintaining strict patient confidentiality. For instance, in October 2024, The Cancer AI Alliance is formed through collaboration between Fred Hutchinson Cancer Center, Dana-Farber Cancer Institute, Memorial Sloan Kettering Cancer Center, Sidney Kimmel Comprehensive Cancer Center, and tech giants such as Amazon Web Services, Inc., Microsoft Corporation, NVIDIA Corporation, and Deloitte to advance AI-driven cancer care, to advance AI-driven cancer care through federated learning, which allows secure, decentralized data collaboration without sharing sensitive patient information.

In remote areas, federated learning is enabling the deployment of AI models directly on edge devices such as wearables and smartphones for health monitoring. These devices can process local data without requiring continuous internet access, making them ideal for low-connectivity environments. Instead of sending raw data, only model updates are shared with central servers, ensuring data privacy. This approach allows for real-time analysis of health metrics, such as heart rate or glucose levels, directly on the device. Federated learning allows models to continually improve with data from multiple devices without compromising user privacy. This is particularly beneficial for managing chronic conditions or providing preventative healthcare in underserved regions. Ultimately, it reduces the reliance on centralized infrastructure while enhancing the accessibility of AI-powered healthcare.

Healthcare institutions are rapidly adopting AI-driven technologies to enhance patient care. Federated learning offers a secure method for training AI models across multiple institutions without sharing sensitive data. This decentralized approach ensures that patient privacy is maintained while enabling collaboration. Allowing data to remain local, federated learning fosters innovation while maintaining security. It also enables AI models to be trained on diverse datasets, improving their accuracy and applicability across various healthcare settings. For instance, in December 2024, Siemens Healthineers, a healthcare technology company in Germany, collaborated with NVIDIA Corporation to integrate MONAI Deploy into their medical imaging platforms. This collaboration aims to accelerate the deployment of AI-driven solutions in clinical settings, making it easier for healthcare institutions to implement advanced AI technologies in medical imaging workflows.

Global Federated Learning In Healthcare Market Report Segmentation

This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends and opportunities in each of the sub-segments from 2018 to 2030. For this study, Grand View Research has segmented the global federated learning in healthcare market report based on application, deployment mode, end-use, and region:

  • Application Outlook (Revenue, USD Million, 2018 - 2030)
  • Medical Imaging
  • Drug Discovery and Development
  • Electronic Health Records (EHR) Analysis
  • Remote Patient Monitoring
  • Clinical Trials
  • Deployment Mode Outlook (Revenue, USD Million, 2018 - 2030)
  • On-premise
  • Cloud-based
  • End-use Outlook (Revenue, USD Million, 2018 - 2030)
  • Hospitals and Healthcare Providers
  • Pharmaceutical and Biotechnology Companies
  • Research Institutions
  • Government and Regulatory Bodies
  • Regional Outlook (Revenue, USD Million, 2018 - 2030)
  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • UK
    • Germany
    • France
  • Asia Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
  • Latin America
    • Brazil
  • Middle East & Africa (MEA)
    • KSA
    • UAE
    • South Africa

Table of Contents

Chapter 1. Methodology and Scope

  • 1.1. Market Segmentation & Scope
  • 1.2. Market Definition
  • 1.3. Information Procurement
    • 1.3.1. Purchased Database
    • 1.3.2. GVR's Internal Database
    • 1.3.3. Secondary Sources & Third-Party Perspectives
    • 1.3.4. Primary Research
  • 1.4. Information Analysis
    • 1.4.1. Data Analysis Models
  • 1.5. Market Formulation & Data Visualization
  • 1.6. Data Validation & Publishing

Chapter 2. Executive Summary

  • 2.1. Market Insights
  • 2.2. Segmental Outlook
  • 2.3. Competitive Outlook

Chapter 3. Federated Learning in Healthcare Market Variables, Trends & Scope

  • 3.1. Global Federated Learning in Healthcare Market Outlook
  • 3.2. Industry Value Chain Analysis
  • 3.3. Market Dynamics
    • 3.3.1. Market Driver Analysis
    • 3.3.2. Market Restraint Analysis
    • 3.3.3. Industry Challenges
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Supplier Power
    • 3.4.2. Buyer Power
    • 3.4.3. Substitution Threat
    • 3.4.4. Threat from New Entrant
    • 3.4.5. Competitive Rivalry
  • 3.5. PESTEL Analysis
    • 3.5.1. Political Landscape
    • 3.5.2. Economic Landscape
    • 3.5.3. Social Landscape
    • 3.5.4. Technological Landscape
    • 3.5.5. Environmental Landscape
    • 3.5.6. Legal Landscape

Chapter 4. Federated Learning in Healthcare Market: Application Estimates & Forecasts

  • 4.1. Federated Learning in Healthcare Market: Application Movement Analysis, 2024 & 2030
    • 4.1.1. Medical Imaging
      • 4.1.1.1. Medical Imaging Market estimates and forecast, 2018 - 2030 (USD Million)
    • 4.1.2. Drug Discovery and Development
      • 4.1.2.1. Drug Discovery and Development Market estimates and forecast, 2018 - 2030 (USD Million)
    • 4.1.3. Electronic Health Records (EHR) Analysis
      • 4.1.3.1. Electronic Health Records (EHR) Analysis Market estimates and forecast, 2018 - 2030 (USD Million)
    • 4.1.4. Remote Patient Monitoring
      • 4.1.4.1. Remote Patient Monitoring Market estimates and forecast, 2018 - 2030 (USD Million)
    • 4.1.5. Clinical Trials
      • 4.1.5.1. Clinical Trials Analysis Market estimates and forecast, 2018 - 2030 (USD Million)

Chapter 5. Federated Learning in Healthcare Market: Deployment Mode Estimates & Forecasts

  • 5.1. Federated Learning in Healthcare Market: Deployment Mode Movement Analysis, 2024 & 2030
    • 5.1.1. On-Premise
      • 5.1.1.1. On-Premise Market estimates and forecast, 2018 - 2030 (USD Million)
    • 5.1.2. Cloud-Based
      • 5.1.2.1. Cloud-Based Market estimates and forecast, 2018 - 2030 (USD Million)

Chapter 6. Federated Learning in Healthcare Market: End Use Outlook Estimates & Forecasts

  • 6.1. Federated Learning in Healthcare Market: End Use Movement Analysis, 2024 & 2030
    • 6.1.1. Hospitals and Healthcare Providers
      • 6.1.1.1. Hospitals and Healthcare Providers Market estimates and forecast, 2018 - 2030 (USD Million)
    • 6.1.2. Pharmaceutical and Biotechnology Companies
      • 6.1.2.1. Pharmaceutical and Biotechnology Companies Market estimates and forecast, 2018 - 2030 (USD Million)
    • 6.1.3. Research Institutions
      • 6.1.3.1. Research Institutions Market estimates and forecast, 2018 - 2030 (USD Million)
    • 6.1.4. Government and Regulatory Bodies
      • 6.1.4.1. Government and Regulatory Bodies Market estimates and forecast, 2018 - 2030 (USD Million)

Chapter 7. Federated Learning in Healthcare Market: Regional Estimates & Trend Analysis

  • 7.1. Federated Learning in Healthcare Market Share, By Region, 2024 & 2030, USD Million
  • 7.2. North America
    • 7.2.1. North America Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.2.1.1. North America Federated Learning in Healthcare Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
      • 7.2.1.2. North America Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.2.1.3. North America Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.2.1.4. North America Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.2.2. U.S.
      • 7.2.2.1. U.S. Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.2.2.2. U.S. Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.2.2.3. U.S. Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.2.2.4. U.S. Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.2.3. Canada
      • 7.2.3.1. Canada Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.2.3.2. Canada Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.2.3.3. Canada Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.2.3.4. Canada Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.2.4. Mexico
      • 7.2.4.1. Mexico Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.2.4.2. Mexico Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.2.4.3. Mexico Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.2.4.4. Mexico Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
  • 7.3. Europe
    • 7.3.1. Europe Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.3.1.1. Europe Federated Learning in Healthcare Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
      • 7.3.1.2. Europe Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.3.1.3. Europe Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.3.1.4. Europe Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.3.2. UK
      • 7.3.2.1. UK Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.3.2.2. UK Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.3.2.3. UK Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.3.2.4. UK Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.3.3. Germany
      • 7.3.3.1. Germany Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.3.3.2. Germany Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.3.3.3. Germany Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.3.3.4. Germany Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.3.4. France
      • 7.3.4.1. France Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.3.4.2. France Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.3.4.3. France Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.3.4.4. France Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
  • 7.4. Asia Pacific
    • 7.4.1. Asia Pacific Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.4.1.1. Asia Pacific Federated Learning in Healthcare Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
      • 7.4.1.2. Asia Pacific Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.4.1.3. Asia Pacific Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.4.1.4. Asia Pacific Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.4.2. China
      • 7.4.2.1. China Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.4.2.2. China Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.4.2.3. China Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.4.2.4. China Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.4.3. Japan
      • 7.4.3.1. Japan Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.4.3.2. Japan Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.4.3.3. Japan Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.4.3.4. Japan Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.4.4. India
      • 7.4.4.1. India Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.4.4.2. India Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.4.4.3. India Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.4.4.4. India Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.4.5. Australia
      • 7.4.5.1. Australia Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.4.5.2. Australia Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.4.5.3. Australia Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.4.5.4. Australia Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.4.6. South Korea
      • 7.4.6.1. South Korea Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.4.6.2. South Korea Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.4.6.3. South Korea Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.4.6.4. South Korea Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
  • 7.5. Latin America
    • 7.5.1. Latin America Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.5.1.1. Latin America Federated Learning in Healthcare Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
      • 7.5.1.2. Latin America Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.5.1.3. Latin America Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.5.1.4. Latin America Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.5.2. Brazil
      • 7.5.2.1. Brazil Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.5.2.2. Brazil Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.5.2.3. Brazil Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.5.2.4. Brazil Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
  • 7.6. Middle East and Africa
    • 7.6.1. Middle East and Africa Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.6.1.1. Middle East and Africa Federated Learning in Healthcare Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
      • 7.6.1.2. Middle East and Africa Federated Learning in Healthcare Market Estimates and Forecasts, by Application , 2018 - 2030 (USD Million)
      • 7.6.1.3. Middle East and Africa Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.6.1.4. Middle East and Africa Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.6.2. UAE
      • 7.6.2.1. UAE Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.6.2.2. UAE Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.6.2.3. UAE Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.6.2.4. UAE Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.6.3. KSA
      • 7.6.3.1. KSA Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.6.3.2. KSA Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.6.3.3. KSA Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.6.3.4. KSA Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 7.6.4. South Africa
      • 7.6.4.1. South Africa Federated Learning in Healthcare Market Estimates and Forecasts, 2018 - 2030 (USD Million)
      • 7.6.4.2. South Africa Federated Learning in Healthcare Market Estimates and Forecasts, by Application, 2018 - 2030 (USD Million)
      • 7.6.4.3. South Africa Federated Learning in Healthcare Market Estimates and Forecasts, by Deployment Mode, 2018 - 2030 (USD Million)
      • 7.6.4.4. South Africa Federated Learning in Healthcare Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)

Chapter 8. Competitive Landscape

  • 8.1. Recent Developments & Impact Analysis, By Key Market Participants
  • 8.2. Vendor Landscape
    • 8.2.1. Company categorization
    • 8.2.2. List of Key Distributors and channel Partners
    • 8.2.3. List of Potential Customers/Listing
  • 8.3. Competitive Dynamics
    • 8.3.1. Competitive Benchmarking
    • 8.3.2. Strategy Mapping
    • 8.3.3. Heat Map Analysis
  • 8.4. Company Profiles/Listing
    • 8.4.1. FedML
      • 8.4.1.1. Participant's overview
      • 8.4.1.2. Financial performance
      • 8.4.1.3. Deployment Mode benchmarking
      • 8.4.1.4. Strategic initiatives
    • 8.4.2. GE Healthcare
      • 8.4.2.1. Participant's overview
      • 8.4.2.2. Financial performance
      • 8.4.2.3. Deployment Mode benchmarking
      • 8.4.2.4. Strategic initiatives
    • 8.4.3. Google LLC
      • 8.4.3.1. Participant's overview
      • 8.4.3.2. Financial performance
      • 8.4.3.3. Deployment Mode benchmarking
      • 8.4.3.4. Strategic initiatives
    • 8.4.4. Health Catalyst
      • 8.4.4.1. Participant's overview
      • 8.4.4.2. Financial performance
      • 8.4.4.3. Deployment Mode benchmarking
      • 8.4.4.4. Strategic initiatives
    • 8.4.5. IBM Corporation
      • 8.4.5.1. Participant's overview
      • 8.4.5.2. Financial performance
      • 8.4.5.3. Deployment Mode benchmarking
      • 8.4.5.4. Strategic initiatives
    • 8.4.6. Medtronic
      • 8.4.6.1. Participant's overview
      • 8.4.6.2. Financial performance
      • 8.4.6.3. Deployment Mode benchmarking
      • 8.4.6.4. Strategic initiatives
    • 8.4.7. Microsoft
      • 8.4.7.1. Participant's overview
      • 8.4.7.2. Financial performance
      • 8.4.7.3. Deployment Mode benchmarking
      • 8.4.7.4. Strategic initiatives
    • 8.4.8. NVIDIA Corporation
      • 8.4.8.1. Participant's overview
      • 8.4.8.2. Financial performance
      • 8.4.8.3. Deployment Mode benchmarking
      • 8.4.8.4. Strategic initiatives
    • 8.4.9. Owkin, Inc.
      • 8.4.9.1. Participant's overview
      • 8.4.9.2. Financial performance
      • 8.4.9.3. Deployment Mode benchmarking
      • 8.4.9.4. Strategic initiatives
    • 8.4.10. Siemens Healthineers
      • 8.4.10.1. Participant's overview
      • 8.4.10.2. Financial performance
      • 8.4.10.3. Deployment Mode benchmarking
      • 8.4.10.4. Strategic initiatives
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