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

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