Edge AI for Healthcare: TinyML for Medical Wearables

Day 1: Model Compression Basics

• Why Model Compression Matters
• Challenges of deploying deep learning models on edge devices
• Trade-offs: Accuracy vs. efficiency
• Compression Techniques Overview
• Pruning (structured/unstructured)
• Quantization (8-bit, binary networks)
• Knowledge distillation basics
• Hands-on Compression Lab
• Applying pruning and quantization using TensorFlow Lite or PyTorch Mobile
• Benchmarking latency and model size

Day 2: On-Device Arrhythmia Classification – Case Study

• Introduction to ECG & Arrhythmia Detection
• ECG signal properties and common arrhythmia types
• Importance of real-time edge inference in cardiology
• End-to-End Model Workflow
• Data preprocessing and labeling (MIT-BIH dataset)
• Model selection (CNN, LSTM for time-series classification)
• Deployment & Evaluation
• Model deployment on microcontrollers (Arduino, EdgeTPU)
• Performance metrics: accuracy, latency, power use

Day 3: Privacy-Preserving Federated Updates

• Fundamentals of Federated Learning
• Decentralized model training across edge devices
• Comparison with traditional cloud-based learning
• Ensuring Data Privacy
• Differential privacy in federated updates
• Homomorphic encryption & secure aggregation techniques
• Use Case Simulation
• Implementing a federated training round using PySyft or Flower
• Simulating data from multiple hospitals for arrhythmia classification

• Students & Researchers in AI, biomedical engineering, or healthcare technologies
• Ph.D. Scholars working on TinyML, federated learning, or wearable health monitoring
• Faculty & Academicians teaching AI, embedded systems, or digital health
• MedTech Developers & Engineers building real-time, low-power medical AI devices
• AI/ML Professionals interested in model compression and on-device inference
• Healthcare Innovators focused on privacy-preserving, intelligent wearable solutions