Federated Learning Solutions for Distributed Healthcare Data
Service Description
Federated Learning (FL) is a transformative approach to Artificial Intelligence that allows models to learn from data without that data ever leaving its original location. In the healthcare sector, where patient privacy and data security are paramount, this service enables organizations to collaborate and train powerful AI models across multiple hospitals or clinics while keeping sensitive records safely behind their own firewalls. This service provides a comprehensive framework for designing, prototyping, and implementing distributed AI solutions. Many high-impact healthcare applications are currently stalled because moving data to a central server is either technically impossible due to bandwidth or legally restricted by GDPR and the AI Act. Our service bridges this gap by bringing the learning to the data. Technical Note: Federated Learning is used exclusively during the training phase. Once training is complete, the resulting AI model is a standard, standalone tool that can be deployed and used at any location at any time, identically to a traditionally trained model. We work closely with you to investigate the feasibility of federated learning for your specific datasets, ensuring that the resulting AI models are both high-performing and privacy-compliant. As this service is closely linked to our Privacy-Preserving Methods and Implementation offering, we integrate advanced techniques such as differential privacy or secure multi-party computation to ensure that even the model updates themselves do not leak sensitive information.
How can the service help you? • Unlock Siloed Data: Train AI on distributed datasets that cannot be moved or merged due to legal (GDPR) or technical constraints. • Enhance Privacy: Minimize data exposure by keeping raw patient records on-premises. • Collaborative Innovation: Enable multiple SMEs or healthcare providers to co-develop a robust Global Model that performs better than any Local Model trained in isolation. • Regulatory Compliance: Align your AI development with the strict requirements of the AI Act and healthcare data regulations.
How the service will be delivered Logistics: The service is delivered through the RISE Applied AI Center and can utilize the V-Platform (a Trusted Research Environment). It can be configured for on-site deployment within your infrastructure or via a cloud-federated setup. Delivery period: Engagements are planned in phases, starting with a feasibility study and moving into prototyping and implementation. Duration: Typically ranges from 3 to 6 months, depending on the complexity of the data architecture and the AI model requirements. Customer requirement: The customer must provide access to relevant use cases, problem statements, and data documentation. While raw data stays with the customer, technical access for the FL-client software is required. Deliveries: Prototyping of the FL architecture, integration of privacy-preserving hooks, and technical documentation. Output: A functional federated learning pipeline and a refined AI model optimized for distributed environments.
This service is highly modular and can be customized to match your specific industry standards, technical infrastructure, and the specific sensitivity level of your health data.
Provider & Contact
Pricing is available to registered users. SMEs receive significant state-aid reductions (GBER) — or, depending on the call, free services during the funded project. Sign in or register to see the price for your organisation.
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