On-Device AI Models

Privacy-first clinical AI processing with edge computing and local model deployment

On-Device AI Models - Edge computing and local processing for healthcare AI with privacy-first architecture

Privacy-First Edge AI Computing

SynThera's on-device AI models bring clinical intelligence directly to the point of care, eliminating the need for cloud connectivity while maintaining the highest levels of privacy and security. Our edge computing architecture deploys lightweight yet powerful AI models locally on medical devices, workstations, and mobile platforms, ensuring patient data never leaves the healthcare facility while providing real-time clinical insights with sub-millisecond latency.

Core Edge AI Capabilities

Local Processing

Complete AI inference on-device with zero cloud dependency

Model Optimization

Quantized and pruned models for efficient edge deployment

Real-Time Inference

Sub-millisecond prediction latency for critical care scenarios

Federated Learning

Collaborative learning without centralized data sharing

Performance Metrics

Inference Speed

<1ms average latency

Real-time processing capability

Model Efficiency

95% size reduction

Maintained 99.2% accuracy

Resource Usage

<100MB memory footprint

Energy-efficient inference

Device Compatibility

ARM, x86, GPU acceleration

iOS, Android, Windows, Linux

Edge Computing Architecture

📱

Mobile Devices

iOS and Android deployment with CoreML and TensorFlow Lite

💻

Workstations

Desktop deployment with GPU acceleration and ONNX runtime

🏥

Medical Devices

Embedded systems integration with specialized hardware

☁️

Edge Servers

On-premise deployment for multi-device coordination

On-Device Model Integration

// On-Device Model Deployment
const edgeModel = new SynTheraEdgeModel({
  modelPath: './models/clinical-prediction-v2.onnx',
  platform: 'ios',
  accelerationMode: 'neural-engine'
});

// Initialize local model
await edgeModel.initialize({
  optimization: {
    quantization: 'int8',
    pruning: 0.3,
    batchSize: 1
  },
  
  privacy: {
    dataEncryption: true,
    modelEncryption: true,
    telemetryDisabled: true
  },
  
  performance: {
    maxMemoryMB: 100,
    maxInferenceMs: 50,
    energyOptimized: true
  }
});

// Process patient data locally
const localPrediction = await edgeModel.predict({
  patientData: {
    vitals: { hr: 85, bp: '120/80', temp: 98.6 },
    symptoms: ['chest-pain', 'fatigue'],
    demographics: { age: 65, gender: 'M' }
  },
  
  processing: {
    realTime: true,
    confidenceThreshold: 0.85,
    explanationsEnabled: true
  }
});

// Federated learning update (privacy-preserving)
await edgeModel.federatedUpdate({
  gradientUpload: false, // Only share model improvements
  differentialPrivacy: true,
  encryptedAggregation: true,
  
  updateFrequency: 'weekly',
  participationOptional: true
});

// Offline functionality
const offlineResult = await edgeModel.predictOffline({
  cacheModel: true,
  fallbackMethods: ['rule-based', 'statistical'],
  qualityAssurance: true
});

console.log('Local prediction:', localPrediction.result);
console.log('Confidence score:', localPrediction.confidence);
console.log('Processing time:', localPrediction.latencyMs + 'ms');
console.log('Privacy preserved:', localPrediction.privacyCompliant);

Model Optimization Techniques

1

Quantization

FP32 to INT8 conversion reducing model size by 75%

2

Pruning

Eliminate redundant parameters while maintaining accuracy

3

Knowledge Distillation

Transfer learning from large models to compact versions

4

Hardware Acceleration

GPU, NPU, and specialized processor optimization

Deployment Platforms

Mobile Platforms

  • • iOS: CoreML with Neural Engine acceleration
  • • Android: TensorFlow Lite with GPU delegate
  • • React Native: Cross-platform deployment
  • • Progressive Web Apps: WebAssembly models

Desktop Systems

  • • Windows: DirectML and ONNX Runtime
  • • macOS: CoreML with M-series optimization
  • • Linux: TensorRT and OpenVINO support
  • • Docker: Containerized edge deployment

Embedded Systems

  • • ARM Cortex processors with NEON
  • • NVIDIA Jetson edge computing
  • • Intel Movidius neural compute sticks
  • • Custom ASIC deployment ready

Privacy-Preserving Edge Intelligence

100%

Local Processing

Patient data never leaves the device or healthcare facility

Zero

Cloud Dependency

Complete offline functionality with no internet required

AES-256

Model Encryption

Encrypted models protect intellectual property and prevent tampering

Privacy Guarantees

  • • No patient data transmission to cloud
  • • Differential privacy for federated learning
  • • Homomorphic encryption for secure computation
  • • Secure multi-party computation support
  • • Local anonymization and pseudonymization
  • • GDPR Article 25 privacy by design compliance

Security Features

  • • Model integrity verification with digital signatures
  • • Tamper-evident deployment packages
  • • Runtime attestation and secure enclaves
  • • Hardware-backed keystores integration
  • • Anti-reverse engineering protections
  • • Secure model updates with rollback capability

Federated Learning Network

🤝

Collaborative Learning

Multiple institutions contribute to model improvement without sharing data

🔒

Privacy Preservation

Differential privacy ensures individual patient data remains confidential

📊

Model Aggregation

Secure aggregation protocols combine learning improvements

🎯

Personalization

Local adaptation to specific patient populations and practice patterns

Federated Learning Implementation

// Federated Learning Configuration
const federatedClient = new SynTheraFederatedClient({
  hospitalId: 'hospital-123',
  privacyLevel: 'maximum',
  encryptionEnabled: true
});

// Participate in federated training
await federatedClient.joinFederatedTraining({
  modelVersion: 'clinical-prediction-v3',
  
  privacySettings: {
    differentialPrivacy: {
      epsilon: 1.0,  // Privacy budget
      delta: 1e-5,   // Privacy parameter
      clipping: 1.0  // Gradient clipping
    },
    
    secureAggregation: true,
    homomorphicEncryption: true,
    patientConsent: 'explicit'
  },
  
  contributionLimits: {
    maxPatients: 1000,
    trainingRounds: 10,
    dataRetention: '30-days'
  },
  
  qualityThresholds: {
    minAccuracy: 0.92,
    maxBiasVariance: 0.05,
    convergenceThreshold: 0.001
  }
});

// Local model training with privacy preservation
const localTraining = await federatedClient.trainLocal({
  localDataset: patientRecords,
  
  preprocessing: {
    anonymization: true,
    featureNormalization: true,
    outlierRemoval: true
  },
  
  training: {
    epochs: 5,
    batchSize: 32,
    learningRate: 0.001,
    regularization: 'l2'
  },
  
  validation: {
    crossValidationFolds: 5,
    holdoutPercentage: 0.2,
    stratification: true
  }
});

// Submit encrypted gradients for aggregation
await federatedClient.submitUpdate({
  encryptedGradients: localTraining.gradients,
  modelMetrics: {
    accuracy: localTraining.accuracy,
    loss: localTraining.loss,
    patientCount: localTraining.sampleSize
  },
  
  qualityAssurance: {
    biasCheck: true,
    fairnessMetrics: true,
    adversarialRobustness: true
  }
});

Edge Computing Benefits

98%

Latency Reduction

Sub-millisecond inference compared to cloud-based processing

100%

Uptime Guarantee

No internet dependency ensures continuous availability

80%

Cost Reduction

Lower bandwidth and cloud computing costs

Zero

Data Breaches

Local processing eliminates transmission vulnerabilities

Deploy Private AI at the Edge

Experience the future of privacy-first clinical AI with on-device processing