헬스케어 연합학습 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the federated learning in healthcare market size is projected to be USD 74.54 million in 2025, USD 86.91 million in 2026, and reach USD 200.59 million by 2031, growing at a CAGR of 18.21% from 2026 to 2031.

This report is Segmented by Component (Software Platforms, Infrastructure, Services), Deployment Mode (On-Premises, Cloud-Based, Hybrid), Application (Drug Discovery, Medical Imaging, EHR Analytics, Remote Monitoring, Clinical Trials), End User (Hospitals, Pharma & Biotech, and More), and Geography (North America, Europe, Asia-Pacific, MEA, South America). Market Forecasts are in Value (USD).

Global Federated Learning In Healthcare Market Trends and Insights

Privacy Regulation-Driven Decentralized AI Adoption

Data protection rules have moved from background compliance work into core architecture decisions across the Federated learning in healthcare market. Hospitals and health networks in the United States and Europe are under rising pressure to avoid raw patient-data transfers across institutional and national boundaries, which makes centralized AI training harder to justify in clinical settings. A 2026 study in Scientific Reports showed that a federated framework using differential privacy and homomorphic encryption reduced membership-inference risk from 20% to 5% against non-private federated baselines, which supports the case for privacy-safe model development without a major utility tradeoff. European policy is pushing this shift further because the EHDS creates a formal structure for secondary use and cross-border access that aligns more naturally with distributed analytics than with raw data pooling. This is creating multi-year software buying cycles as providers look for platforms that already contain auditability, traceability, and governance controls. For smaller hospitals and regional systems, the Federated learning in healthcare market is especially attractive when vendors can absorb legal, technical, and workflow complexity through managed deployment models.

Multi-Institution Imaging Model Scaling Without Data Pooling

Imaging has become one of the clearest early demand centers in the Federated learning in healthcare market because radiology and pathology data are both valuable and difficult to share. Traditional multi-site imaging collaborations often slow down at contracting, liability review, and local governance approval, which can delay or stop pooled-data model training. A February 2026 study in npj Digital Medicine found that combining federated learning with a traveling-model approach across multiple sites reduced misclassification disparities from 34% to 26% while also improving balanced accuracy, which shows that distributed training can improve both robustness and fairness. GE HealthCare's work with Mass General Brigham and the University of Wisconsin-Madison on a 3D MR foundation model trained on more than 200,000 multi-site images shows that health systems are starting to treat imaging archives as shared training infrastructure rather than isolated local assets. That raises the bar for vendors that still depend on single-site datasets, especially in radiology and pathology, where performance gaps can become visible quickly. As a result, the Federated learning in healthcare market is seeing imaging-led adoption not only because privacy matters, but also because model quality now improves when more institutions can participate without surrendering raw scans.

Non-IID Clinical Data Heterogeneity and Model Drift

The most persistent technical restraint in the federated learning in healthcare market is the mismatch between clinical datasets collected at different sites. Hospitals vary in imaging protocols, device vendors, coding behavior, patient mix, and label quality, so standard aggregation methods do not always deliver a model that generalizes evenly across all participants. Research from the Federated Tumor Segmentation challenge, published in Nature Communications in April 2025, found that adaptive aggregation algorithms could perform well on average across a federation while still showing meaningful performance drops at specific institutions. That means average federation performance can mask local risk, which is a serious issue when clinical teams need dependable results at each hospital rather than only a strong pooled benchmark. Early 2026 work on drift-aware fine-tuning points in a better direction, but it also shows that site-level tuning sensitivity remains a live issue in medical settings. As a result, the federated learning in healthcare market still favors well-resourced academic centers and large health systems that can maintain per-site monitoring, recalibration, and validation workflows over time.

Other drivers and restraints analyzed in the detailed report include:

1. Biopharma Demand for Privacy-Safe Collaborative Drug Discovery
2. Cloud and Confidential Computing Stack Maturity
3. Legacy EHR And PACS Integration Burden

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software platforms held 52.38% of component revenue in 2025, which gave this layer the largest position in the Federated learning in healthcare market. The leading demand driver was the need for orchestration, model aggregation, privacy tooling, and governance functions that hospitals could procure as a supported product instead of assembling internally. Procurement behavior also favored software because many providers wanted a turnkey environment that could fit existing compliance processes without requiring a deep in-house federated engineering team. Open-source frameworks such as NVIDIA FLARE, Flower, and PySyft have widened technical access, but they have also increased pressure on commercial vendors to differentiate through workflow integration, monitoring, auditability, and implementation support.

Services is the fastest-growing component segment, with the federated learning in healthcare market size for services projected to expand at 19.16% CAGR between 2026 and 2031. That pace reflects the fact that many institutions can buy software, but still need outside help for configuration, validation, governance mapping, training operations, and ongoing MLOps support. Managed delivery is becoming more important because healthcare buyers want accountable deployment outcomes rather than only access to a software license. This is also why service scope is moving past implementation into runtime operations, as vendors increasingly support inference routing, monitoring, and federation management across distributed environments. Over time, the Federated learning in healthcare market is likely to see services narrow the distance with software revenue as providers treat federated AI as managed infrastructure rather than as a tool set they operate entirely on their own.

On-premises deployment held 57.61% share in 2025, which made it the leading mode in the federated learning in healthcare market. That result reflected long-standing hospital preferences for keeping protected health information inside institutional boundaries and extending existing PACS, storage, and local GPU investments into new AI workflows. For many academic medical centers, on-premises deployment was a natural continuation of earlier imaging analytics and research infrastructure rather than a completely new capital decision. Risk officers also tended to prefer local control because it simplified internal approval and reduced concern over third-party access during model development.

Cloud-based deployment is the fastest-growing mode, with the federated learning in healthcare market size for cloud-based deployment projected to expand at 18.83% CAGR through 2031. Growth is being supported by confidential computing tools that let hospitals protect data during active processing and verify workload integrity in shared infrastructure. Europe is also creating a regulatory pathway for cloud-resident secure processing environments as health data access frameworks mature, which lowers policy uncertainty for providers evaluating hybrid and remote orchestration models. Hybrid deployment is therefore gaining ground across multi-site networks that want cloud-level coordination while still keeping local computation and raw data on site. In the Federated learning in healthcare industry, deployment choices are becoming less about cloud versus local ideology and more about which model best matches governance, scale, and integration readiness.

Geography Analysis

North America held 41.42% of the federated learning in healthcare market share in 2025, which made it the largest regional contributor. The region benefits from a dense concentration of academic medical centers, established HIPAA-driven governance processes, and early commercial deployment of federated platforms in healthcare and life sciences. The US remains the core revenue center because it combines strong AI infrastructure, active enterprise software adoption, and visible production examples such as NVIDIA FLARE in biomedical and pharmaceutical settings. Canada is also contributing through research-led collaboration models, while Mexico remains earlier in adoption because hospital digitization and advanced analytics infrastructure are less mature across many provider environments.

Europe is gaining structural weight in the federated learning in healthcare market because policy design is directly shaping infrastructure demand. Regulation (EU) 2025/327 established the European Health Data Space, set up Health Data Access Bodies in each member state, and formalized HealthData@EU for cross-border secondary use, which makes federated infrastructure central to future data access workflows. Germany has moved faster than most peers in national preparation, building on the Gesundheitsdatennutzungsgesetz and related work around research data infrastructure and interoperability roles. This gives Europe a different market profile from North America because adoption is not only enterprise-led, it is also being shaped by public regulatory architecture. That dynamic should support procurement of governance-rich platforms, secure processing environments, and cross-border federation tools over the forecast period.

Asia-Pacific is the fastest-growing region, and the federated learning in healthcare market size for Asia-Pacific is projected to expand at 18.61% CAGR through 2031. South Korea's March 2026 plan for a national medical data space shows how public policy is starting to mirror the federated model by keeping raw data inside each hospital while enabling secure multi-institutional AI development. Taiwan is also notable because its national healthcare federated learning initiative uses NVIDIA FLARE, which shows that state-backed adoption can accelerate once a common orchestration layer is selected. China offers large potential, but local data rules favor architectures that preserve domestic control and often support on-premises federation rather than broader cross-border pooling. India and Australia remain earlier-stage markets centered on academic medical centers and cancer research networks, while the Middle East and Africa demand is being shaped by GCC digital health spending, and South America is led by Brazil under privacy conditions that resemble European-style data protection expectations.

1. Duality Technologies Inc.
2. FedML Inc.
3. Flower Labs GmbH
4. Fujitsu
5. GE HealthCare Technologies Inc.
6. Google LLC
7. Health Catalyst
8. IBM
9. Intel
10. Johnson & Johnson
11. Koninklijke Philips
12. Lifebit Biotech Ltd.
13. Medtronic
14. Microsoft
15. NVIDIA
16. Owkin
17. Rhino Federated Computing
18. Roche
19. Secure AI Labs Inc.
20. Siemens Healthineers

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Privacy Regulation-Driven Decentralized AI Adoption
  • 4.2.2 Multi-Institution Imaging Model Scaling Without Data Pooling
  • 4.2.3 Biopharma Demand for Privacy-Safe Collaborative Drug Discovery
  • 4.2.4 Cloud and Confidential Computing Stack Maturity
  • 4.2.5 EHDS-Enabled Cross-Border Secondary-Use Pathways
  • 4.2.6 Federated AI Registries and Algorithmic Vigilance Networks
• 4.3 Market Restraints
  • 4.3.1 Non-IID Clinical Data Heterogeneity and Model Drift
  • 4.3.2 Legacy EHR And PACS Integration Burden
  • 4.3.3 Site-Level GPU, MLOps, and Networking Cost Burden
  • 4.3.4 Model IP, Liability, And Contributor-Value Allocation Disputes
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 Market Size & Growth Forecasts

• 5.1 By Component
  • 5.1.1 Software Platforms
  • 5.1.2 Infrastructure Solutions
  • 5.1.3 Services
• 5.2 By Deployment Mode
  • 5.2.1 On-Premises
  • 5.2.2 Cloud-Based
  • 5.2.3 Hybrid
• 5.3 By Application
  • 5.3.1 Drug Discovery & Development
  • 5.3.2 Medical Imaging & Diagnostics
  • 5.3.3 Electronic Health Record & Clinical Data Analytics
  • 5.3.4 Remote Patient Monitoring
  • 5.3.5 Clinical Trial Optimization
• 5.4 By End User
  • 5.4.1 Hospitals & Health Systems
  • 5.4.2 Pharmaceutical & Biotechnology Companies
  • 5.4.3 Research & Academic Institutions
  • 5.4.4 Diagnostic Laboratories & Imaging Networks
  • 5.4.5 Contract Research Organizations
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Spain
    • 5.5.2.6 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 India
    • 5.5.3.4 Australia
    • 5.5.3.5 South Korea
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 Middle East & Africa
    • 5.5.4.1 GCC
    • 5.5.4.2 South Africa
    • 5.5.4.3 Rest of Middle East & Africa
  • 5.5.5 South America
    • 5.5.5.1 Brazil
    • 5.5.5.2 Argentina
    • 5.5.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global level Overview, Market-level Overview, Core Segments, Financials, Strategic Information, Market Rank/Share, Products & Services, Recent Developments)
  • 6.3.1 Duality Technologies Inc.
  • 6.3.2 FedML Inc.
  • 6.3.3 Flower Labs GmbH
  • 6.3.4 Fujitsu Limited
  • 6.3.5 GE HealthCare Technologies Inc.
  • 6.3.6 Google LLC
  • 6.3.7 Health Catalyst Inc.
  • 6.3.8 IBM Corporation
  • 6.3.9 Intel Corporation
  • 6.3.10 Johnson & Johnson
  • 6.3.11 Koninklijke Philips N.V.
  • 6.3.12 Lifebit Biotech Ltd.
  • 6.3.13 Medtronic plc
  • 6.3.14 Microsoft Corporation
  • 6.3.15 NVIDIA Corporation
  • 6.3.16 Owkin
  • 6.3.17 Rhino Federated Computing
  • 6.3.18 Roche Holding AG
  • 6.3.19 Secure AI Labs Inc.
  • 6.3.20 Siemens Healthineers AG

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment