의료 분야 연합학습 시장 : 시장 규모 분석(용도별, 전개 방식별, 최종 용도별, 지역별)과 예측(2022-2032년)

The Global Federated Learning in Healthcare Market is valued at approximately USD 24.85 billion in 2023 and is poised to grow at a remarkable compound annual growth rate (CAGR) of 16.00% over the forecast period 2024-2032. Federated learning is rapidly emerging as a cornerstone technology in the digital transformation of healthcare systems. It empowers organizations to collaboratively train machine learning models across decentralized data sources-such as hospitals, clinics, and diagnostic labs-without the need to centralize sensitive patient data. This breakthrough not only bolsters data privacy and compliance with strict healthcare regulations such as HIPAA and GDPR, but also enhances the scalability and efficiency of AI-driven diagnostics, clinical research, and personalized medicine development.

The accelerating shift towards value-based care, combined with the exponential rise in health data generated by wearables, EMRs, and medical imaging systems, has amplified the demand for secure, privacy-preserving AI frameworks. Federated learning addresses this challenge head-on by enabling multi-institutional collaborations without compromising data ownership. Major healthcare providers, research institutes, and tech giants are increasingly adopting federated learning platforms to uncover complex disease patterns and predictive insights. Nevertheless, the technology's mass adoption faces hurdles, particularly in standardizing interoperable systems, maintaining model accuracy across non-IID data distributions, and ensuring real-time model synchronization at scale.

Innovations in edge computing and homomorphic encryption are rapidly converging with federated learning, allowing secure computation across nodes without raw data ever leaving its source. These integrations are facilitating real-time analytics for critical applications such as early disease detection, patient risk stratification, and clinical decision support. Moreover, the cloud-based deployment of federated learning frameworks has significantly reduced entry barriers for smaller healthcare institutions and startups, making it more accessible across tier-2 and tier-3 regions. These advancements are opening up new frontiers in collaborative AI research and enabling healthcare organizations to deploy robust, predictive models with minimal infrastructure overhead.

As stakeholders increasingly recognize the strategic importance of federated learning, a surge in partnerships and R&D investments is reshaping the competitive landscape. Tech innovators are building customizable, scalable platforms embedded with differential privacy, blockchain authentication, and federated analytics toolkits tailored for healthcare applications. Meanwhile, regulatory bodies and industry consortiums are working towards creating standardized protocols to streamline federated learning implementation across public and private health networks. These developments are expected to fortify trust, improve deployment efficiency, and reduce technological friction in the years ahead.

From a geographical standpoint, North America dominates the federated learning in healthcare market, largely owing to the early adoption of AI in clinical workflows, supportive government initiatives, and a well-established digital health ecosystem. Europe follows suit, buoyed by strong regulatory backing for data privacy and a robust academic research framework. The Asia Pacific region is forecasted to witness the highest growth rate during the forecast period, fueled by rapid healthcare digitization, expanding mobile health infrastructure, and increasing investments in AI from countries like China, India, and South Korea. Latin America and the Middle East & Africa are gradually catching up, propelled by pilot programs and international collaborations focused on enhancing healthcare delivery through privacy-preserving AI.

Major market player included in this report are:

• NVIDIA Corporation
• Intel Corporation
• Google LLC
• IBM Corporation
• Microsoft Corporation
• Amazon Web Services, Inc.
• Cloudera, Inc.
• Edge Delta
• Owkin, Inc.
• Sherpa.ai
• Consilient Inc.
• TensorFlow (Google Brain)
• HPE (Hewlett Packard Enterprise)
• Duality Technologies
• Arm Ltd

The detailed segments and sub-segment of the market are explained below:

By Application

• Medical Imaging
• Drug Discovery
• Disease Prediction
• Remote Patient Monitoring
• Others

By Deployment Mode

• On-premise
• Cloud-based

By End-use

• Hospitals
• Research Centers
• Diagnostics Laboratories
• Pharmaceutical & Biotechnology Companies
• Others

By Region:

• North America
• U.S.
• Canada
• Europe
• UK
• Germany
• France
• Spain
• Italy
• ROE
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• RoAPAC
• Latin America
• Brazil
• Mexico
• Middle East & Africa
• Saudi Arabia
• South Africa
• RoMEA

Years considered for the study are as follows:

• Historical year - 2022
• Base year - 2023
• Forecast period - 2024 to 2032

Key Takeaways:

• Market Estimates & Forecast for 10 years from 2022 to 2032.
• Annualized revenues and regional level analysis for each market segment.
• Detailed analysis of geographical landscape with Country level analysis of major regions.
• Competitive landscape with information on major players in the market.
• Analysis of key business strategies and recommendations on future market approach.
• Analysis of competitive structure of the market.
• Demand side and supply side analysis of the market.

Table of Contents

Chapter 1. Global Federated Learning in Healthcare Market Executive Summary

• 1.1. Global Federated Learning in Healthcare Market Size & Forecast (2022-2032)
• 1.2. Regional Summary
• 1.3. Segmental Summary
  • 1.3.1. By Application
  • 1.3.2. By Deployment Mode
  • 1.3.3. By End-use
• 1.4. Key Trends
• 1.5. Recession Impact
• 1.6. Analyst Recommendation & Conclusion

Chapter 2. Global Federated Learning in Healthcare Market Definition and Research Assumptions

• 2.1. Research Objective
• 2.2. Market Definition
• 2.3. Research Assumptions
  • 2.3.1. Inclusion & Exclusion
  • 2.3.2. Limitations
  • 2.3.3. Supply Side Analysis
    • 2.3.3.1. Availability
    • 2.3.3.2. Infrastructure
    • 2.3.3.3. Regulatory Environment
    • 2.3.3.4. Market Competition
    • 2.3.3.5. Economic Viability (Consumer's Perspective)
  • 2.3.4. Demand Side Analysis
    • 2.3.4.1. Regulatory Frameworks
    • 2.3.4.2. Technological Advancements
    • 2.3.4.3. Environmental Considerations
    • 2.3.4.4. Consumer Awareness & Acceptance
• 2.4. Estimation Methodology
• 2.5. Years Considered for the Study
• 2.6. Currency Conversion Rates

Chapter 3. Global Federated Learning in Healthcare Market Dynamics

• 3.1. Market Drivers
  • 3.1.1. Rising Proliferation of Health Data Sources
  • 3.1.2. Heightened Focus on Data Privacy and Compliance
  • 3.1.3. Demand for Collaborative AI in Clinical Research
• 3.2. Market Challenges
  • 3.2.1. Model Accuracy across Non-IID Data Distributions
  • 3.2.2. Technical Complexity of Secure Aggregation
  • 3.2.3. Integration with Legacy Healthcare Systems
• 3.3. Market Opportunities
  • 3.3.1. Edge-Computing-Enabled Federated Frameworks
  • 3.3.2. Homomorphic Encryption and Secure Multiparty Computation
  • 3.3.3. Expansion into Emerging Healthcare Markets

Chapter 4. Global Federated Learning in Healthcare Market Industry Analysis

• 4.1. Porter's 5 Force Model
  • 4.1.1. Bargaining Power of Suppliers
  • 4.1.2. Bargaining Power of Buyers
  • 4.1.3. Threat of New Entrants
  • 4.1.4. Threat of Substitutes
  • 4.1.5. Competitive Rivalry
  • 4.1.6. Futuristic Approach to Porter's 5 Force Model
  • 4.1.7. Porter's 5 Force Impact Analysis
• 4.2. PESTEL Analysis
  • 4.2.1. Political
  • 4.2.2. Economical
  • 4.2.3. Social
  • 4.2.4. Technological
  • 4.2.5. Environmental
  • 4.2.6. Legal
• 4.3. Top Investment Opportunities
• 4.4. Top Winning Strategies
• 4.5. Disruptive Trends
• 4.6. Industry Expert Perspective
• 4.7. Analyst Recommendation & Conclusion

Chapter 5. Global Federated Learning in Healthcare Market Size & Forecasts by Application 2022-2032

• 5.1. Segment Dashboard
• 5.2. Application Revenue Trend Analysis, 2022 & 2032

Chapter 6. Global Federated Learning in Healthcare Market Size & Forecasts by Deployment Mode 2022-2032

• 6.1. Segment Dashboard
• 6.2. Deployment Mode Revenue Trend Analysis, 2022 & 2032

Chapter 7. Global Federated Learning in Healthcare Market Size & Forecasts by End-use 2022-2032

• 7.1. Segment Dashboard
• 7.2. End-use Revenue Trend Analysis, 2022 & 2032

Chapter 8. Global Federated Learning in Healthcare Market Size & Forecasts by Region 2022-2032

• 8.1. North America Market
  • 8.1.1. U.S. Market
    • 8.1.1.1. Application breakdown size & forecasts, 2022-2032
    • 8.1.1.2. Deployment Mode breakdown size & forecasts, 2022-2032
  • 8.1.2. Canada Market
• 8.2. Europe Market
  • 8.2.1. UK Market
  • 8.2.2. Germany Market
  • 8.2.3. France Market
  • 8.2.4. Spain Market
  • 8.2.5. Italy Market
  • 8.2.6. Rest of Europe Market
• 8.3. Asia Pacific Market
  • 8.3.1. China Market
  • 8.3.2. India Market
  • 8.3.3. Japan Market
  • 8.3.4. Australia Market
  • 8.3.5. South Korea Market
  • 8.3.6. Rest of Asia Pacific Market
• 8.4. Latin America Market
  • 8.4.1. Brazil Market
  • 8.4.2. Mexico Market
  • 8.4.3. Rest of Latin America Market
• 8.5. Middle East & Africa Market
  • 8.5.1. Saudi Arabia Market
  • 8.5.2. South Africa Market
  • 8.5.3. Rest of Middle East & Africa Market

Chapter 9. Competitive Intelligence

• 9.1. Key Company SWOT Analysis
  • 9.1.1. NVIDIA Corporation
  • 9.1.2. Intel Corporation
  • 9.1.3. Google LLC
• 9.2. Top Market Strategies
• 9.3. Company Profiles
  • 9.3.1. NVIDIA Corporation
    • 9.3.1.1. Key Information
    • 9.3.1.2. Overview
    • 9.3.1.3. Financial (Subject to Data Availability)
    • 9.3.1.4. Product Summary
    • 9.3.1.5. Market Strategies
  • 9.3.2. Intel Corporation
  • 9.3.3. Google LLC
  • 9.3.4. IBM Corporation
  • 9.3.5. Microsoft Corporation
  • 9.3.6. Amazon Web Services, Inc.
  • 9.3.7. Cloudera, Inc.
  • 9.3.8. Edge Delta
  • 9.3.9. Owkin, Inc.
  • 9.3.10. Sherpa.ai
  • 9.3.11. Consilient Inc.
  • 9.3.12. TensorFlow (Google Brain)
  • 9.3.13. HPE (Hewlett Packard Enterprise)
  • 9.3.14. Duality Technologies
  • 9.3.15. Arm Ltd

Chapter 10. Research Process

• 10.1. Research Process
  • 10.1.1. Data Mining
  • 10.1.2. Analysis
  • 10.1.3. Market Estimation
  • 10.1.4. Validation
  • 10.1.5. Publishing
• 10.2. Research Attributes