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

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.

Application Insights

The drug discovery and development segment dominated the federated learning in healthcare industry with a share of 34.0% in 2024. Healthcare institutions are increasingly integrating AI technologies to improve diagnostics and treatment. Federated learning enables collaborative AI model training without the need to share sensitive patient data. This approach keeps data local, preserving privacy and meeting regulatory requirements. Federated learning maintains security and enables safe, collaborative innovation across healthcare institutions. It also allows models to learn from diverse datasets, improving performance and generalizability in clinical settings.

Remote patient monitoring is experiencing significant growth in the market for federated learning in healthcare. This growth is fueled by advancements in wearable devices, IoT sensors, and telehealth technologies. It allows continuous tracking of patient health outside clinical settings, offering doctors real-time access to vital data. The approach supports early intervention, reduces hospital readmissions, and improves chronic disease management. As a result, it enhances patient outcomes, increases convenience for both patients and providers, and helps reduce overall healthcare costs. Moreover, remote monitoring supports aging populations, enabling care in rural or underserved areas.

Deployment Mode Insights

The on-premise segment accounted for the largest revenue share in 2024. The on-premise deployment mode often chosen by organizations with strict privacy and compliance requirements. On-premise setups allow sensitive patient data to remain within local servers, reducing external exposure. These deployments typically require substantial infrastructure and IT support. Still, many healthcare institutions prefer this approach to ensure data sovereignty and customized security measures. As data privacy regulations become more stringent, the demand for secure on-premise solutions is expected to grow.

The cloud-based segment is experiencing significant growth in the federated learning in healthcare industry, driven by the growing adoption of cloud-based healthcare solutions that offer scalability, flexibility, and remote access to data. Cloud platforms enable seamless integration of electronic health records (EHRs), AI tools, and remote monitoring systems. They also support secure data storage and real-time collaboration among healthcare providers. As digital transformation accelerates in healthcare, the demand for cloud infrastructure continues to rise. This trend is further supported by increasing investments in health IT and the need for cost-effective data management solutions.

End-use Insights

The hospitals and healthcare providers segment generated the highest revenue share in 2024, driven by the increasing adoption of AI, remote monitoring, and digital health technologies in clinical settings. Hospitals are investing in federated learning to enhance data privacy while accessing insights from various sources. These technologies improve diagnostics, treatment planning, and overall patient care. The rising demand for personalized, secure healthcare solutions has helped this segment maintain its leading position. Moreover, federated learning enables hospitals to collaborate without sharing sensitive data, ensuring confidentiality while expanding AI capabilities. As digital transformation continues, hospitals are expected to be a key driver of innovation in the healthcare sector.

Pharmaceutical and biotechnology companies are experiencing significant growth in the federated learning healthcare market. These companies are utilizing AI and machine learning to accelerate drug discovery and development. Federated learning allows them to collaborate on research while maintaining the privacy of sensitive data. This approach enables better insights into clinical trials, genomics, and personalized medicine. As a result, pharmaceutical and biotech firms are increasingly adopting these technologies to enhance research outcomes and improve treatment options.

Regional Insights

North America dominated the federated learning in healthcare industry and accounted for a 34.4% share in 2024. The region benefits from advanced healthcare infrastructure and widespread adoption of digital health technologies. Strong investments in AI, data privacy regulations, and healthcare innovation further contribute to its market leadership. As healthcare providers and research institutions in North America continue to adopt federated learning, the region is expected to maintain its dominant position in the market.

U.S. Federated Learning in Healthcare Market Trends

The U.S. holds a significant share of the federated learning in healthcare industry, driven by its advanced healthcare infrastructure and extensive investments in AI technologies. Regulatory frameworks, including HIPAA, are well-aligned with the privacy-preserving capabilities of federated learning. Many healthcare providers, pharmaceutical companies, and research institutions in the U.S. are adopting federated learning to enhance data privacy and improve clinical research outcomes.

Europe Federated Learning in Healthcare Market Trends

Europe has been a dominant player in the federated learning in healthcare industry, largely due to its strong data privacy regulations, such as the General Data Protection Regulation (GDPR). Countries such as Germany, the UK, and France are at the forefront of adopting federated learning for secure healthcare collaborations. European healthcare institutions are increasingly using federated learning to comply with privacy standards while improving patient care and research efficiency.

Asia Pacific Federated Learning in Healthcare Market Trends

The Asia Pacific region is experiencing rapid growth in the federated learning in healthcare industry, driven by increasing investments in AI and data privacy technologies. Countries such as China, Japan, and South Korea are making significant strides in healthcare innovation, leveraging federated learning to securely collaborate across institutions. The region’s growing demand for advanced healthcare solutions, coupled with a large population base, is fostering an environment ripe for federated learning adoption.

Key Federated Learning In Healthcare Company Insights

Some of the key companies in the federated learning in healthcare industry include GE Healthcare, IBM Corporation, and Health Catalyst. Organizations are focusing on increasing their customer base to gain a competitive edge in the industry. Therefore, key players are taking several strategic initiatives, such as mergers and acquisitions, and partnerships with other major companies.

• Health Catalyst is actively involved in federated learning to enhance healthcare data analytics. Their platform integrates advanced AI techniques, including federated learning, to enable healthcare organizations to collaborate on data-driven insights while maintaining data privacy. Utilizing federated learning, Health Catalyst supports the secure sharing of medical data for predictive analytics and decision-making, helping improve patient care across different healthcare institutions.

• IBM Corporation has been a key player in the development of federated learning in healthcare through its Watson Health platform. IBM focuses on creating privacy-preserving AI solutions that allow healthcare organizations to collaborate on training machine learning models without sharing sensitive patient data. Their work in federated learning aimed to advancing personalized medicine, improving clinical outcomes, and enhancing the efficiency of healthcare systems while complying with data privacy regulations.

Recent Developments

• In January 2025, Owkin, Inc., a biotech company in France, launched K1.0 Turbigo, an advanced operating system designed to accelerate drug discovery and diagnostics using AI and multimodal patient data from its federated network. This system powers biological insights and supports major pharmaceutical collaborations, with plans for K2.0 to integrate autonomous AI agents for future lab research and development.

• In October 2024, Owkin, Inc., announced a partnership with AstraZeneca to develop an AI-powered tool for pre-screening germline BRCA mutations (gBRCAm) in breast cancer patients. This partnership focuses on creating a solution that can analyze digitized pathology slides to identify patients who may benefit from further genetic testing, thereby facilitating earlier and more accurate diagnosis.

• In March 2023, FedML, a U.S.-based company offering decentralized, privacy-focused AI tools, partnered with Konica Minolta to bring decentralized and privacy-preserving AI to healthcare by enabling collaborative training and deployment of machine learning models without centralizing data. This approach helps overcome regulatory and data-sharing challenges, allowing institutions to unlock the potential of siloed medical data for improved diagnostics and treatment.

Federated Learning In Healthcare Market Report Scope

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