Infrastructure

What is data infrastructure?

Data infrastructure refers to the foundational systems, technologies, and processes that enable the secure, reliable, and efficient handling of data throughout its lifecycle. In the context of federated learning (FL), infrastructure plays a dual role: it supports local data storage and processing, while also enabling coordinated, privacy-aware model training across multiple institutions.

This includes hardware (e.g. local servers, GPUs), software (e.g. data management tools, model orchestration platforms), network connections, and technical standards that ensure interoperability, scalability, and security in a distributed setting.

Why is data infrastructure important?

Robust infrastructure is essential to operationalizing federated learning and sustaining cross-institutional collaboration. It ensures that:

• Data remains securely stored at each institution and is never transferred unnecessarily
• Model training and updates can be orchestrated across nodes
• Communication between participants is secure, stable, and efficient
• Legal, ethical, and institutional requirements are supported
• Systems can scale with increasing data volume, partner sites, or model complexity
• FAIR principles are upheld through use of standardized protocols and metadata practices -Downtime, data loss, and technical errors are minimized through resilient design

Without the right infrastructure, federated learning can become infeasible or insecure, especially in environments with diverse technical capacities.

What should be considered for data infrastructure?

To enable effective and trustworthy federated learning, infrastructure should address the following:

• Security: Use encryption (in transit and at rest), secure authentication, and monitoring to protect data and models.

• Scalability: Choose infrastructure that can adapt to growing datasets, participants, and model complexity.

• Interoperability: Adopt standards-based APIs, metadata schemas, and communication protocols to support cross-site compatibility.

• Local Processing: Ensure each site has the necessary compute resources to train models locally (e.g. containers, VMs, or secure enclaves).

• Federated Orchestration: Deploy a platform that manages distributed training and aggregation (e.g. Flower, Substra, Fed-BioMed).

• Backup & Recovery: Implement site-level backup and disaster recovery procedures to prevent data loss.

• Sustainability: Plan for ongoing maintenance, updates, and funding of infrastructure components.

• Documentation: Maintain clear records of infrastructure setups, roles, responsibilities, and access points.

• Compliance: Ensure all systems align with legal and ethical obligations (e.g. GDPR, HIPAA, institutional policies).

• FAIR Enablement: Support persistent identifiers, standardized formats, and well-documented metadata to promote FAIR data handling.

• Performance Monitoring: Track infrastructure performance, latency, and reliability to quickly detect and resolve bottlenecks.

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