An open-source platform advancing medical AI via privacy-preserving swarm learning, based on NVFlare and developed with the ODELIA consortium.
Current release: v1.5.0 (swarm-robustness).
MediSwarm is a federated / swarm learning framework for medical imaging. It lets hospitals and research institutions collaboratively train deep-learning models on distributed datasets without ever sharing raw patient data. Each site trains locally inside a Docker container, and only model updates (gradients or weights) leave the institution — coordinated by an NVIDIA FLARE (NVFlare) 2.7.2 server.
MediSwarm currently ships two production pipelines — ODELIA for 3D breast MRI classification and STAMP for computational pathology — and is designed to be extended with new tasks through a simple compatibility interface (see Making Your Training Code MediSwarm-Compatible).
Choose your role and follow the instructions:
- Swarm Participant — ODELIA / Breast MRI
- Swarm Participant — STAMP / Computational Pathology
- Developer (Docker, Code, Pipeline)
- Swarm Operator (Provisioning, VPN, Server)
- Making Your Training Code MediSwarm-Compatible
The diagram below shows how MediSwarm connects multiple hospital sites through an NVFlare server. Each site runs training inside its own Docker container; only model updates travel over the network.
graph TB
subgraph "NVFlare Server"
S[FL Server<br/>Orchestration]
A[FedAvg / FedOpt<br/>Aggregator]
S --- A
end
subgraph "Site 1 — Hospital A"
D1[Docker Container<br/>ODELIA / STAMP]
DATA1[(Local Medical<br/>Data)]
D1 --- DATA1
end
subgraph "Site 2 — Hospital B"
D2[Docker Container<br/>ODELIA / STAMP]
DATA2[(Local Medical<br/>Data)]
D2 --- DATA2
end
subgraph "Site 3 — Hospital C"
D3[Docker Container<br/>ODELIA / STAMP]
DATA3[(Local Medical<br/>Data)]
D3 --- DATA3
end
D1 -- "model updates ↑↓ global model" --> S
D2 -- "model updates ↑↓ global model" --> S
D3 -- "model updates ↑↓ global model" --> S
style S fill:#4a90d9,stroke:#2c5f8a,color:#fff
style A fill:#5ba55b,stroke:#3a7a3a,color:#fff
style D1 fill:#e8a838,stroke:#c4872a,color:#fff
style D2 fill:#e8a838,stroke:#c4872a,color:#fff
style D3 fill:#e8a838,stroke:#c4872a,color:#fff
style DATA1 fill:#ddd,stroke:#999
style DATA2 fill:#ddd,stroke:#999
style DATA3 fill:#ddd,stroke:#999
A federated training run proceeds in rounds. The server sends the current global model to all sites, each site trains locally, and the server aggregates the returned updates.
sequenceDiagram
participant Server as NVFlare Server
participant SiteA as Site A (Docker)
participant SiteB as Site B (Docker)
participant SiteC as Site C (Docker)
Server->>Server: Initialize global model
loop Each Training Round
Server->>SiteA: Send global model
Server->>SiteB: Send global model
Server->>SiteC: Send global model
par Local Training
SiteA->>SiteA: Train on local data
SiteB->>SiteB: Train on local data
SiteC->>SiteC: Train on local data
end
SiteA->>Server: Return model updates
SiteB->>Server: Return model updates
SiteC->>Server: Return model updates
Server->>Server: Aggregate updates (FedAvg)
Server->>Server: Evaluate global model
end
Server->>SiteA: Distribute final model
Server->>SiteB: Distribute final model
Server->>SiteC: Distribute final model
| Pipeline | Domain | Input | Backbone(s) | Docker Image | Python | PyTorch |
|---|---|---|---|---|---|---|
| ODELIA | Breast MRI classification | 3D NIfTI volumes | DenseNet-121, DINOv2 | jefftud/odelia:1.5.0 | 3.10 | 2.2.2 |
| STAMP | Computational pathology (classification / survival) | H5 feature files | VIT, MLP, TransMIL | jefftud/stamp:<version> | 3.11 | 2.7.1 |
ODELIA — A 3D CNN pipeline for ternary classification on breast MRI, originating from the ODELIA consortium. Uses DenseNet-121 as the default backbone with optional DINOv2 feature extraction.
STAMP — Solid Tumor Associative Modeling in Pathology, based on KatherLab STAMP 2.4.0. Operates on pre-extracted H5 feature files (not raw whole-slide images), supporting classification and survival prediction tasks with multiple attention-based backbones.
- Privacy by design — raw data never leaves the hospital; only model weights/gradients are transmitted
- Dockerized reproducibility — every site runs the identical container image, eliminating environment drift
- NVFlare 2.7.2 — built on NVIDIA's production-grade federated learning
platform (local fork in
docker_config/NVFlare/) - Multi-pipeline — supports diverse imaging modalities (3D MRI, pathology features) from a single framework
- Provisioning & VPN — turnkey provisioning YAML files and OpenVPN guides for secure multi-site deployments
- CI/CD — automated PR tests, integration tests, and Docker image builds via GitHub Actions
- Live sync —
kit_live_sync/utilities for real-time startup kit synchronization across sites - Evaluation toolkit — scripts for benchmarking, log parsing, AUROC
plotting, and prediction (
scripts/evaluation/)
MediSwarm/
├── application/
│ ├── jobs/ # NVFlare job definitions
│ │ ├── ODELIA_ternary_classification/
│ │ ├── STAMP_classification/
│ │ ├── cifar10/ # Reference / smoke-test job
│ │ ├── minimal_training_pytorch_cnn/
│ │ └── _shared/ # Shared job components
│ └── provision/ # Provisioning YAML files
│ ├── project_Odelia_allsites.yml
│ ├── project_HA.yml
│ └── ...
├── docker_config/
│ ├── Dockerfile_ODELIA # ODELIA image (Python 3.10)
│ ├── Dockerfile_STAMP # STAMP image (Python 3.11)
│ ├── Dockerfile_cifar10 # Minimal test image
│ ├── NVFlare/ # Local NVFlare 2.7.2 fork
│ └── master_template.yml
├── scripts/
│ ├── build/ # Docker build & startup-kit scripts
│ ├── ci/ # CI helpers (integration tests, apt updates)
│ ├── evaluation/ # Benchmarking, log parsing, AUROC plots
│ └── client_node_setup/ # Site-level setup utilities
├── docs/ # Extended documentation
├── tests/
│ ├── integration_tests/ # End-to-end NVFlare simulation tests
│ └── unit_tests/ # Model & config unit tests
├── assets/ # Role-specific READMEs, diagrams
├── server_tools/ # Server management utilities
├── kit_live_sync/ # Startup-kit live synchronization
├── workspace/ # Runtime workspace (gitignored)
├── odelia_image.version # Current version (1.2.0)
├── pyproject.toml
├── deploy_and_test.sh # One-click deploy & test
└── README.md
MIT — see LICENSE.
Contributions welcome! Open an issue or submit a PR.
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