Apheris and T-Systems Launch Federated Data Network
The partnership uses federated learning to process clinical research data across European sites without moving patient records.
Updated on Oct. 27, 2026 in Artificial Intelligence
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Apheris and T-Systems have enabled federated data network operations on T-Systems infrastructure to support secure clinical research. This framework allows researchers to train AI models locally while maintaining data privacy compliance across European hospitals.
Why it matters
General Data Protection Regulation requirements often complicate multi-site clinical research by restricting the movement of sensitive patient files. Federated learning addresses these barriers by bringing models to local environments, enabling collaboration without transferring raw data.
The architecture utilizes NVIDIA FLARE for federated learning, connecting clinical data from more than 50,000 patients. The system processes locally trained parameters within the T-Systems Industrial AI Cloud for global model aggregation.
The players
Apheris
A company that provides software for federated data networks used in drug discovery and medical research.
T-Systems
A subsidiary of Deutsche Telekom that offers information technology services and Industrial AI Cloud infrastructure.
NVIDIA
A multinational technology company known for designing graphics processing units and advanced AI computing platforms.
The details
Researchers utilized this infrastructure for a 2025 feasibility study involving 512 MRI cases to perform MS lesion segmentation. Nine pharmaceutical companies currently participate in the AI Structural Biology network leveraging these GPU resources.
Timeline
A feasibility study on MS lesion segmentation was conducted in 2025.
The NVIDIA GPU Technology Conference takes place in Berlin from October 20 to 22, 2026.
The Tech Race
The shift toward federated learning marks a departure from centralized data storage models which are increasingly incompatible with stringent global privacy laws. This technology positions the collaboration to lead in secure, multi-institutional clinical research environments.
Patients benefit from increased data privacy as their medical records are processed locally rather than shared across institutional servers. Researchers gain faster access to large-scale clinical insights while maintaining adherence to international protection standards.
The takeaway
Federated learning allows sensitive medical data to remain at the source while still providing researchers with actionable insights. This architectural change is likely to become the standard for large-scale pharmaceutical and clinical collaborations globally.
Further reading
For more developments in this field, visit our Artificial Intelligence section.
Source note: This article includes information reported by TelecomDrive.
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