BC Platforms signs Strategic Collaboration Agreement with AWS to accelerate healthcare and life sciences innovation
BC Platforms signs a Strategic Collaboration Agreement with AWS to scale secure, AI-powered research environments.
BC Platforms partners with Flower Labs to enable federated machine learning across trusted research environments
Federated learning with the Flower SuperGrid is available within BC Platforms’ trusted research environments, enabling secure AI development across distributed clinical, real‑world, and multi‑modal data.
BC Platforms has partnered with Flower Labs to enable federated machine learning within our trusted research environments (TRE), which support collaborative model development across institutions.
Building on the Flower SuperGrid, this integration allows organizations to deploy and run machine learning across sites while keeping sensitive healthcare data securely at the source. Flower SuperGrid is powered by the Flower Framework, an open-source federated learning framework from Flower Labs, is widely used to enable machine learning (ML) across distributed data without centralizing sensitive information.
Why this matters to research teams
Machine learning is used across healthcare organizations to analyze medical images, predict disease progression, identify drug targets, and support clinical and research decision‑making. Developing these models requires access to large, diverse datasets, which are often held across hospitals, research institutions, and countries.
This data cannot be centralized due to privacy regulations, patient consent requirements, and data sovereignty constraints. Federated learning enables models to be trained across distributed data without moving it. By enabling Flower SuperGrid within trusted research environments, BC Platforms and Flower Labs make it possible to develop and scale models while keeping data securely at the source.
Train models across data held at multiple institutions
Real-world and clinical data is held across institutions and cannot be centralized due to regulatory, consent, and governance constraints. BC Platforms’ TRE, BC Mosaic, enables secure governed access to data across sites through a federated approach.
With the integration of Flower SuperGrid in BC Mosaic, organizations can build on this foundation to develop machine learning models across those same sites. Each organization can deploy a Flower SuperNode within its environment, keeping data under local control while SuperGrid coordinates federated learning workflows across participating sites.
Run federated machine learning within your existing environment
Federated learning is widely used to work across distributed data, but often requires dedicated infrastructure, custom integration, and coordination across participating sites. By enabling Flower SuperGrid within our trusted research environments, this capability can be deployed within existing infrastructure. Organizations can activate it and connect to other participating sites using established governance, security, and access controls.
This removes the need to build project-specific infrastructure that can be reused across collaborations, programs, and therapeutic areas.
Across Europe, we are seeing strong momentum as healthcare organizations prepare for the EHDS. Trusted research environments are a critical foundation for secure data access, but the next step is enabling collaboration across them at scale. By integrating federated machine learning within these environments, BC Platforms enables organizations to develop models across institutions without moving data while meeting the strict governance, control, and compliance requirements needed for real-world deployment.
Karl Quinn, Global Head of Alliances & Channels, BC PLatforms
Remove friction from multi-site AI development
One of the main barriers to developing machine learning models across institutions is not access to data, but the effort required to access, align, and govern it. This involves negotiating access to datasets, aligning legal and compliance requirements, and building technical infrastructure for each project.
By combining the BC Platforms TRE with Flower SuperGrid, organizations can participate in multi-site model development using environments and governance already in place. This allows research teams to focus on developing models and generating insights rather than managing data access, legal constraints, and technical integration.
Train and validate models across institutions
With federated machine learning deployed within a TRE, organizations can:
- Train models across hospitals, academic institutions, and industry partners
- Validate models on datasets that remain locally governed and managed
- Utilize NVIDIA GPU and software stack as part of your ML / AI workflows
- Leverage AWS Bedrock, SageMaker, and other ML / AI services
- Participate in multi-institutional research collaborations at scale
This is particularly relevant for:
- Pharmaceutical and biotech companies conducting multi-country studies across disparate datasets
- Academic research networks and consortia coordinating analyses across institutions
- Healthcare organizations preparing for cross-institutional data collaboration
- New ML / AI models running on existing datasets to uncover new findings
Scalable infrastructure for federated AI
Federated machine learning within BC Platforms’ trusted research environments is supported by cloud‑native infrastructure on AWS, NVIDIA‑accelerated computing and Flower SuperGrid. Through BC Mosaic, organizations can access scalable GPU compute and optimized AI and data science frameworks, including NVIDIA RAPIDS, enabling high‑performance model training and validation across distributed clinical, genomic, imaging, and real‑world data.
This combination powers federated learning workflows to scale securely from individual institutions to national or multi‑country collaboration.
This partnership makes privacy‑preserving healthcare AI practical at scale. By bringing models to the data, our collaboration with BC Platforms enables researchers, pharma companies, and healthcare institutions to train and validate models across diverse real‑world datasets while keeping sensitive data securely within trusted research environments. I’m excited to see the new federations BC Platforms customers will form and join through this partnership, as well as the research breakthroughs they can unlock.
Pedro Mesa, Founding Commercial Partner, Flower Labs
Supporting EHDS and European-scale collaboration
Across Europe, healthcare organizations are preparing for the European Health Data Space (EHDS), which will enable secondary use of health data within secure, governed environments.
As more institutions deploy trusted research environments, the ability to connect them and work across them becomes critical. By enabling model development across these environments, BC Platforms and Flower Labs support a model where machine learning can be developed at European scale while data remains under local control. This approach aligns with emerging requirements for cross-border research, data sovereignty, and regulatory-grade evidence generation.
Run federated AI across your data network
Deploy Flower SuperGrid within your BC Platforms trusted research environment and connect with other institutions to develop models without moving sensitive data.