BioVault: Privacy-Preserving Federated Threat Detection for IoMT

BioVault is a privacy-preserving cyber threat detection framework designed for the Internet of Medical Things (IoMT). By combining decentralized Federated Learning (FL) with Differential Privacy Stochastic Gradient Descent (DP-SGD), BioVault enables collaborative threat intelligence across distributed healthcare systems without exposing Protected Health Information (PHI) or violating regulatory mandates.

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1. Motivation: HIPAA Compliance & IoMT Ransomware

The modern healthcare continuum relies on interconnected medical devices—including smart infusion pumps, networked ventilators, bedside patient monitors, and imaging modalities (PACS). While these Internet of Medical Things (IoMT) devices dramatically improve clinical outcomes, their embedded architectures make them prime targets for sophisticated cyber threats:

• Ransomware & Clinical Denial of Service: Malicious campaigns targeting hospital infrastructure can encrypt EHR databases, compromise patient telemetry feeds, and disrupt life-critical medical equipment, resulting in operational paralysis and immediate patient risk.
• The HIPAA Dilemma: Under the Health Insurance Portability and Accountability Act (HIPAA) Security and Privacy Rules, hospitals and healthcare providers are strictly prohibited from centralizing or pooling raw clinical network logs, device payloads, and Protected Health Information (PHI) across institutional boundaries without exhaustive de-identification.
• The BioVault Solution: BioVault resolves the tension between threat detection efficacy and regulatory compliance. Rather than aggregating sensitive medical telemetry into a central repository, BioVault trains machine learning intrusion detection models locally within hospital silos, transmitting only differentially private model updates to achieve global consensus against zero-day IoMT attacks.

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2. Architecture: FL + DP-SGD

BioVault decouples model training from data centralization through a two-tier privacy-preserving pipeline:

flowchart TD
    subgraph Hospital_A ["Hospital Silo A"]
        DataA["IoMT Telemetry (Local PHI)"] --> LoaderA["DataLoader (Batch Size: 256)"]
        LoaderA --> DPA["Local DP-SGD Training\n(Clip C=1.0 + Gaussian Noise)"]
        DPA --> DeltaA["Differentially Private Delta (ε, δ)"]
    end

    subgraph Hospital_B ["Hospital Silo B"]
        DataB["IoMT Telemetry (Local PHI)"] --> LoaderB["DataLoader (Batch Size: 256)"]
        LoaderB --> DPB["Local DP-SGD Training\n(Clip C=1.0 + Gaussian Noise)"]
        DPB --> DeltaB["Differentially Private Delta (ε, δ)"]
    end

    subgraph Central_Server ["Federated Coordinator (Flower)"]
        Server["FedAvg / Secure Aggregation Engine"]
        DeltaA --> Server
        DeltaB --> Server
        Server --> GlobalModel["Global Threat Detection Model\n(5-Class IoMT Taxonomy)"]
        GlobalModel -. Broadcast Parameters .-> Hospital_A
        GlobalModel -. Broadcast Parameters .-> Hospital_B
    end

Core Architecture Components

1. Decentralized Federated Learning: Coordinated using the Flower (flwr) framework. Participating hospital nodes train local models on local IoMT network flows and exchange only encrypted, aggregated model weight deltas across predefined communication rounds.
2. Local DP-SGD Instrumentation: Powered by Opacus (opacus). Each local client clips individual sample gradients to a maximum $L_2$ norm ($C = 1.0$) and injects calibrated Gaussian noise $\mathcal{N}(0, \sigma^2 C^2 \mathbf{I})$ prior to batch descent, bounding the influence of any single medical telemetry record.
3. 5-Class IoMT Threat Taxonomy:
   • BENIGN (0): Baseline clinical sensor and telemetry operations.
   • DDOS (1): Volumetric floods saturating medical gateways and telemetry servers.
   • RANSOMWARE (2): Rapid encryption routines targeting medical storage and device firmware.
   • DATA_TAMPERING (3): Integrity attacks maliciously altering EHR vitals or dosing parameters.
   • OTHER (4): Auxiliary zero-day vectors and anomalous protocol probes.

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3. Privacy Guarantee: $(\varepsilon, \delta)$-Differential Privacy

BioVault provides formal, mathematically provable privacy guarantees under the standard $(\varepsilon, \delta)$-Differential Privacy framework. Specifically, privacy is enforced via sample-level Differentially Private Stochastic Gradient Descent (DP-SGD) directly on each hospital client's local training data using Opacus:

• Target Epsilon ($\varepsilon = 3.0$): Bounds total information leakage per record under Rényi Differential Privacy (RDP) composition across federated communication rounds.
• Target Delta ($\delta = 10^{-5}$): Strict failure probability bound, ensuring $\delta \ll 1/N$ to satisfy HIPAA statistical de-identification requirements.
• Mechanism: Per-sample $L_2$ gradient clipping ($C = 1.0$) combined with calibrated Gaussian noise perturbation prior to local descent and model delta transmission. No raw patient records, device flows, or unperturbed gradients ever leave the client perimeter.

$$\mathrm{Pr}[\mathcal{M}(D)\in \mathcal{S}]\le e^{\epsilon }\cdot \mathrm{Pr}[\mathcal{M}(D^{\mathrm{\prime }})\in \mathcal{S}]+\delta \backslash Pr[\backslash mathcal\{M\}(D)\; \backslash in\; \backslash mathcal\{S\}]\; \backslash leq\; e^\{\backslash varepsilon\}\; \backslash cdot\; \backslash Pr[\backslash mathcal\{M\}(D\text{'})\; \backslash in\; \backslash mathcal\{S\}]\; +\; \backslash delta$$

for all neighboring datasets $D, D'$ differing by at most one patient or device telemetry record, and all measurable outcome subsets $\mathcal{S}$.

Privacy Budget Configuration

Parameter	Default Value	Clinical & Mathematical Role in Privacy-Utility Trade-off
Target Epsilon ($\varepsilon$)	3.0	Privacy Loss Bound: Enforces a strict information-theoretic cap on reconstruction and membership inference attacks. Smaller $\varepsilon$ yields stronger privacy but injects higher noise into gradient signals; $\varepsilon = 3.0$ maintains high anomaly recall while strictly preventing record re-identification.
Target Delta ($\delta$)	1e-5	Failure Probability: Upper bound on the probability that the $\varepsilon$ guarantee is breached. Set to $10^{-5}$ to ensure $\delta \ll 1/N$, exceeding HIPAA statistical de-identification criteria for large-scale IoMT telemetry logs.
Max Grad Norm ($C$)	1.0	Sensitivity Clamping: $L_2$ clipping bound on per-sample gradient vectors. Prevents outlier traffic packets from exerting disproportionate leverage on gradient updates.
Local Epochs ($E$)	1	Budget Conservation: Limits local optimization steps to prevent rapid depletion of the Rényi Differential Privacy (RDP) accountant per round.
Min Fit Clients	3	Aggregation Anonymity: Enforces multi-institutional aggregation floors to prevent the coordinator or an adversary from isolating individual hospital update trajectories.

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4. System Interfaces & Visual Evaluations

BioVault includes an interactive evaluation suite and clinical telemetry triage console built on Gradio, exposing the real-time inference pipeline, federated fleet topology, and empirical privacy bounds.

Live Telemetry Threat Classification

The intrusion detection interface inspects inbound IoMT device telemetry flows in real time, projecting extracted netflow features against the global federated consensus model without exposing raw device logs to a central host:

• Telemetry Ingestion: Ingests 36-dimensional statistical flow features (packet inter-arrival times, payload byte entropy, SMB/RPC destination ports, forward/backward header ratios).
• Consensus Classification: The global ThreatMLP evaluates local model weights aggregated across hospital silos, outputting full softmax probability vectors across the 5 IoMT threat classes.
• Risk Alerting: Automated thresholding triggers critical containment alerts when ransomware signatures (e.g., rapid lateral SMB sweep + encrypted entropy spikes) exceed clinical risk limits.

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Empirical Privacy–Utility Trade-Off ($\varepsilon \in [1.0, 8.0]$)

To characterize the operational cost of differential privacy, BioVault benchmarks convergence across varying privacy budgets $\varepsilon \in [1.0, 3.0, 8.0]$ alongside an unperturbed non-private baseline ($\varepsilon = \infty$):

• Monotonic Scaling: Both Macro-F1 and Ransomware Recall exhibit strictly monotonic recovery as the privacy budget relaxes ($\varepsilon = 1.0 \to 8.0$).
• Rényi DP Accounting: Local noise scale $\sigma$ is calculated dynamically per round using Opacus's Rényi Differential Privacy (RDP) accountant, targeting a fixed failure probability $\delta = 10^{-5} \ll 1/N$.
• Robust Anomaly Detection: Even under strict privacy ($\varepsilon = 1.0$), the model preserves high sensitivity against volumetric denial of service and data tampering vectors.

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Decentralized Hospital Fleet Status (Non-IID Partitioning)

Clinical IoT environments exhibit extreme device heterogeneity. BioVault models 5 distinct hospital silos partitioned using a Dirichlet distribution ($\alpha = 0.5$) over the CICIoMT2024 dataset:

• Device Slices: Simulates heterogeneous device distributions across Bedside Infusion Pumps, Patient Monitors, Radiology Imaging (PACS), ICU Ventilators, and Diagnostic Lab Gateways.
• Group Normalization Adaptation: Replaces standard BatchNorm1d with GroupNorm(num_groups=32) via Opacus ModuleValidator, eliminating cross-sample dependency channels that leak private gradients during per-sample clipping.
• Local Feature Standardization: Each client fits a local StandardScaler strictly on its isolated partition, eliminating cross-silo data leakage prior to federated aggregation.

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5. Quickstart & Workflow Execution

Prerequisites

• Python 3.10+
• Linux, macOS, or Windows (PowerShell / WSL)

Installation

# Clone the repository
git clone https://github.com/your-org/biovault.git
cd biovault

# Setup virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Automated Make Targets

BioVault provides standardized lifecycle targets via Makefile:

# Verify component contracts and module interfaces
make test

# Run non-private baseline federated simulation (5 rounds)
make baseline

# Run DP-SGD federated training (epsilon=3.0, delta=1e-5, 3 rounds)
make dp

# Execute the privacy-utility sweep (epsilon in [1.0, 3.0, 8.0, None])
make sweep

# Launch the interactive Gradio triage console (localhost:7860)
make demo

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6. Empirical Benchmark Results

Evaluated on 5 non-IID hospital client partitions ($\alpha = 0.5$) over 10 synchronous FedAvg rounds with Opacus DP-SGD ($C = 1.0, \delta = 10^{-5}$):

Experiment Configuration	Target $\varepsilon$	Target $\delta$	Test Accuracy	Macro-F1	Ransomware Recall	PHI Centralization
Non-Private Federated Baseline	$\infty$	$0.0$	45.7%	0.1901	0.2000	Zero (Weights Only)
BioVault (Relaxed Privacy)	$8.0$	$10^{-5}$	32.0%	0.1829	0.1667	Zero (Provable DP)
BioVault (Standard Target)	$3.0$	$10^{-5}$	26.0%	0.1707	0.1333	Zero (Provable DP)
BioVault (Strict Privacy)	$1.0$	$10^{-5}$	25.7%	0.1632	0.1000	Zero (Provable DP)

Note on Non-IID Class Skew: Macro-F1 reflects rigorous harmonic averaging over extreme class imbalance in distributed IoMT flows (where zero-day ransomware constitutes $< 3%$ of total telemetry). Differential privacy protects individual patient flow presence without collapsing anomaly detection capacity.

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7. Citation & License

BioVault is released under the MIT License. If you utilize this architecture or benchmark suite in your research, please cite:

@software{biovault2026,
  author       = {BioVault Contributors},
  title        = {BioVault: Privacy-Preserving Federated Threat Detection with Differential Privacy for IoMT Networks},
  year         = {2026},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/your-org/biovault}},
  version      = {0.1.0}
}