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Engine 4 of 4 • Federated Learning

OmniWeaveX

Privacy-Preserving Federated Learning

Collaborative AI without data sharing. OmniWeaveX enables multi-site model training with differential privacy and secure aggregation — allowing hospitals and research institutions to build better AI together while keeping patient data local.

ε-δ
Privacy Guarantee
30+
Features
Multi
Site Training
Request Demo → Explore Features
OmniLabelX — Annotation Canvas
OmniLabelX Annotation Canvas
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Privacy
Differential
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Data Stays
Local
The Challenge

Healthcare AI Needs More Data, But Privacy Comes First

Training effective medical AI requires diverse data from multiple institutions. But healthcare data is highly sensitive — bound by privacy regulations, institutional policies, and ethical obligations. Centralizing data isn't an option.

Traditional approaches force a choice: protect privacy or build better AI. Federated learning changes this equation by bringing the computation to the data, not the data to the computation.

  • DPDP Act 2023 mandates data localization within India
  • Hospital networks cannot share raw patient data
  • Rare disease research requires multi-site collaboration
  • Single-site AI models suffer from population bias
OmniLabelX Annotation Canvas
Key Features

Collaborative AI, Zero Data Sharing

OmniWeaveX provides the infrastructure for privacy-preserving collaborative model training across institutions.

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Differential Privacy

Mathematical privacy guarantees (ε-δ) ensure individual patient data cannot be reconstructed from model updates. Configurable privacy budgets per institution.

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Secure Aggregation

Model gradients are encrypted before transmission. Central server aggregates updates without seeing individual contributions. Powered by Flower framework.

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Automated QA

Continuous quality monitoring with Dice > 0.88 and Hausdorff < 5mm thresholds. Automatic model validation before deployment. Alert on quality degradation.

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Explainability

SHAP values, attention maps, and Grad-CAM visualization for every federated model. Understand how each site contributes to model behavior.

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Cross-Site Peer Review

Review challenging cases across institutions without exposing patient identity. Collaborative learning with privacy-preserving case sharing.

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Model Registry

Version control for federated models. Track performance across sites, manage deployments, and rollback if needed. Full audit trail.

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Contribution Fairness

Shapley value-based attribution of each site's contribution to model improvement. Incentivize participation with transparent credit allocation.

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Byzantine Resilience

Protection against malicious or faulty clients. Robust aggregation methods detect and exclude anomalous updates from the federation.

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Heterogeneity Handling

Works with non-IID data distributions across sites. Personalized federated learning adapts global models to local population characteristics.

Architecture

How Federated Learning Works

Data stays local. Only model updates travel. Everyone benefits.

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Local Training

Each hospital trains on their local data. Patient data never leaves the institution's network.

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Encrypt Updates

Model gradients (not data) are encrypted and noise is added for differential privacy.

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Secure Aggregation

Central server aggregates encrypted updates. Cannot see individual site contributions.

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Global Model

Improved global model is distributed back to all sites. Everyone benefits from collective intelligence.

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Federated Learning Architecture
Show hospitals, central server, encrypted data flow
Privacy

Three Pillars of Privacy Protection

Multiple layers of protection ensure patient data remains private at every step.

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Data Locality

Raw patient data never leaves the hospital. All training happens on-premise within the institution's secure network. Full DPDP Act compliance.

100% Local
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Differential Privacy

Calibrated noise is added to model updates before transmission. Mathematically proven privacy guarantees prevent patient re-identification.

ε-δ Guarantee
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Secure Aggregation

Cryptographic protocols ensure the aggregation server cannot inspect individual updates. Only the combined result is decrypted.

Zero-Knowledge
Applications

Ideal for Multi-Site Collaboration

OmniWeaveX powers collaborative AI across healthcare networks and research consortiums.

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Hospital Networks

Apollo, Fortis, Max Healthcare, and other multi-site networks can train unified AI models across all locations without centralizing sensitive patient data.

Network-wide AI Unified Models Data Local
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Research Consortiums

AIIMS, IITs, and medical research institutions can collaborate on rare disease studies and multi-center trials with privacy-preserving data sharing.

Rare Diseases Multi-center Trials Publication Ready
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Cross-Border Studies

Collaborate internationally while complying with data localization requirements. Models cross borders; patient data doesn't.

GDPR Compliant No Data Export Global Insights
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Genomics + Imaging

Combine imaging with genomic data for precision medicine without centralizing ultra-sensitive genetic information. Multi-modal federated learning.

Multi-modal Precision Medicine Privacy First

Sample Federation Network

5 sites collaborating on chest X-ray AI

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AIIMS Delhi
Academic Medical Center
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Apollo Chennai
Private Hospital
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Aggregator
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Fortis Mumbai
Private Hospital
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CMC Vellore
Teaching Hospital
Specifications

Technical Details

Specification Details
Federation Framework Flower (flwr), PySyft integration available
Privacy Mechanism Differential Privacy (Rényi DP), Secure Aggregation (SecAgg)
Communication gRPC over TLS 1.3, optional VPN tunneling
Aggregation Strategies FedAvg, FedProx, FedAdam, custom strategies
Client Requirements Docker container, 8GB RAM, optional GPU
Supported Models PyTorch, TensorFlow, scikit-learn, XGBoost
Quality Thresholds Configurable Dice, Hausdorff, AUC minimums
Explainability SHAP, Grad-CAM, per-site contribution analysis
Audit & Compliance Immutable logs, DPDP compliant, ISO 27001 ready
Deployment Cloud aggregator (India), on-premise clients
Platform Integration

The Collaboration Layer

OmniWeaveX federates models from OmniModelX across institutions. Training data comes from OmniLabelX annotations. Reports from OmniReasonX provide structured inputs.

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OmniLabelX
Federated annotation data
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OmniReasonX
Structured clinical inputs
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OmniModelX
Models to federate

Join the Federation

Collaborate on AI research without compromising patient privacy or data sovereignty. See how OmniWeaveX enables secure multi-site model training.

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