FederateX brings healthcare data standardization and multi-site federated learning together in a single managed platform. Train AI across institutions while sensitive data stays at each site — DT handles the deployment, orchestration, and governance.
From site onboarding and data preparation through federated training and governance — a structured, managed workflow where sensitive data stays at each site throughout.
Each participating site is onboarded with identity and access controls, secure network connectivity, and clear permission boundaries — ensuring only authorized participants join the federation.
Identity · Access · SecurityClinical data at each site is aligned to common healthcare standards such as OMOP, creating a consistent and auditable foundation for cross-site collaboration. DT's OMOP DataHub supports this standardization process as part of the platform's broader data preparation capabilities.
OMOP · Healthcare StandardsVersioned feature contracts define what each site prepares for training — including feature definitions, data quality rules, and labeling standards — so that all participants work from a consistent, reproducible set of inputs.
Reproducible · VersionedEach site trains models locally on its own data using your chosen ML framework. Only model updates are shared with the central platform — sensitive records stay on-site.
Local Compute · Custom ModelsThe FederateX platform combines model updates from all participating sites into an improved global model using configurable aggregation strategies. Privacy-enhancing techniques such as secure aggregation and differential privacy can be layered in as collaboration requirements grow.
Aggregation · Privacy OptionsAudit trails, evidence packs, and governance controls provide traceability for every training run. DT manages this governance layer as part of the platform — supporting compliance reviews and building trust across participating organizations.
Audit · Evidence · ComplianceFederateX is designed with the security, governance, and operational requirements of healthcare organizations in mind.
Centralized identity controls and role-based permissions govern who can access what throughout the lifecycle of every federation — with audit logging that provides a clear record of every access event.
Load custom PyTorch, TensorFlow, or scikit-learn models directly into the platform. FederateX is model-agnostic and adapts to your research and healthcare AI use cases.
Cross-site federated learning depends on consistent inputs. FederateX supports alignment to healthcare standards such as OMOP, with DT's OMOP DataHub helping reduce the data preparation burden — so sites can collaborate on a shared, reliable foundation.
Every training run produces a traceable evidence trail — including run metadata, configuration records, and audit summaries — designed to support internal governance and regulatory review processes.
Encrypted communications, secrets management, and infrastructure hardening form the platform's security baseline. Privacy-enhancing capabilities such as secure aggregation and differential privacy can be integrated as requirements evolve.
DT operates the platform on your behalf — handling job orchestration, monitoring, incident response, and upgrades so your team stays focused on models and outcomes.
Every organization has different data conditions, governance requirements, and infrastructure constraints. FederateX is built to adapt to yours.
data_adapter: omop_v6 — OMOP DataHub
model: custom_readmission_risk_v2.pt
branding: HealthNetwork_A_Theme
feature_groups: [labs_v2, meds_v1, encounters_v3]
governance_mode: regional_network
Federated learning is especially valuable when data is sensitive, distributed across institutions, and too limited at any single site to train effective models alone. These are examples of healthcare AI programs where FederateX is designed to add value.
Train models across multiple sites to predict 30-day readmission risk using labs, medications, and encounter data — leveraging broader patient populations while each institution retains its own records.
Rare adverse drug events are difficult to detect from a single institution's data alone. Federated learning across hospital networks increases the signal available for detection — without centralizing sensitive medication records.
Build clinical language models from distributed EHR notes across institutions — supporting use cases such as clinical text classification, entity extraction, and coding assistance, with training data that never leaves each site.
Collaboratively train radiology and pathology models across imaging centers — building on larger and more diverse cohorts while imaging data remains at each participating site.
FederateX is a managed federated learning platform that DT deploys, operates, and supports — designed for healthcare organizations that need multi-site AI collaboration with clear governance and security foundations.
FederateX is architecturally designed so that sensitive data remains within each participating institution. Model updates — not patient records — are what move between sites and the central platform, providing a foundation for multi-organization collaboration built on trust.
DT understands the complexity of clinical data environments — from standardization and alignment to cross-site consistency. FederateX reflects this understanding, with healthcare data preparation built into the platform's design.
DT configures the platform for your environment, manages the deployment, and continues to operate and support it in production. Your team focuses on healthcare outcomes, not infrastructure.
Imagine training an AI model on patient data from hospitals in Vancouver, Toronto, and Halifax without a single record ever leaving its original institution. No data transfers. No privacy breaches. No regulatory grey zones. Just powerful, collaborative intelligence built at the edges.
— Privacy-First AI: How Federated Learning Is Transforming Canadian Cancer ResearchWorking with DT Consulting Group and Flower Labs, DHDP is developing proof of concept and premium features to create a functional backend for Platform users — showing that Flower AI not only works seamlessly beside current Platform architecture, but also enhances functionality.
— DHDP Tech Update: Flower AI Phase 1 CompleteIf your organization is exploring multi-site healthcare AI, we'd like to hear from you. Our team can walk you through how FederateX works and discuss how it fits your environment.