8.4 Edge AI and Federated Learning

Edge AI and federated learning are revolutionizing IoT systems. By processing data locally on devices, Edge AI reduces latency, improves privacy, and enables real-time decision-making. It's crucial for applications like autonomous vehicles and industrial control systems.

Federated learning allows collaborative model training across decentralized devices without sharing raw data. This preserves privacy, reduces communication overhead, and improves model performance by leveraging diverse data distributions from multiple devices.

Edge AI in IoT Systems

Concepts of edge AI and federated learning

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• Edge AI processes and analyzes data locally on IoT devices (smart sensors) or edge servers (gateways) without relying on cloud communication
  • Reduces latency enables real-time decision making critical for applications (autonomous vehicles, industrial control systems)
  • Improves privacy by keeping sensitive data (medical records, financial information) on the device minimizing security risks
  • Optimizes bandwidth usage minimizes the amount of data transferred to the cloud conserving network resources
  • Increases reliability allows autonomous operation even with limited or intermittent internet connectivity (remote locations, emergency situations)
• Federated learning enables collaborative model training across multiple decentralized devices (smartphones, IoT sensors) without sharing raw data
  • Preserves data privacy raw data remains on individual devices only model updates (gradients, parameters) are shared with a central server
  • Reduces communication overhead only model parameters are exchanged not the entire dataset making it scalable for large-scale deployments
  • Improves model performance by leveraging the collective knowledge and diverse data distributions of multiple devices (different user behaviors, environmental conditions)

Deployment of models on IoT devices

• Model optimization techniques adapt machine learning models to resource-constrained IoT devices
  • Model compression reduces the size of the model while maintaining accuracy
    1. Pruning removes less important weights or connections in the model
    2. Quantization reduces the precision of model parameters (32-bit to 8-bit) to save memory and computation
  • Model distillation trains a smaller student model to mimic the behavior of a larger teacher model
  • Hardware-specific optimizations leverage device-specific instructions (ARM NEON) or accelerators (GPUs, TPUs) to speed up inference
• Edge inference considerations ensure models perform efficiently on IoT devices
  • Resource constraints account for limited memory (kilobytes), storage (megabytes), and processing power (MHz) on IoT devices
  • Energy efficiency optimizes models and inference algorithms for low power consumption (milliwatts) to extend battery life
  • Latency requirements ensure real-time performance (milliseconds) for time-critical applications (industrial control, video analytics)
  • Deployment strategies include over-the-air updates, containerization (Docker), and versioning of models for seamless rollouts and management

Federated Learning in IoT Systems

Implementation of federated learning frameworks

• TensorFlow Federated (TFF) is an open-source framework developed by Google for federated learning
  • Provides a high-level API for defining federated computations and algorithms
  • Supports different federated learning architectures (FedAvg, FedProx) and optimization methods (SGD, Adam)
• PySyft is a Python library for secure and private deep learning including federated learning capabilities
  • Offers a secure multi-party computation (MPC) framework for privacy-preserving model training and inference
  • Integrates with popular deep learning frameworks (PyTorch, TensorFlow) for easy adoption
• LEAF is a benchmark framework for learning in federated settings
  • Provides a suite of datasets (FEMNIST, Shakespeare) and evaluation metrics (accuracy, communication cost) for federated learning research
  • Enables fair comparison and reproducibility of federated learning algorithms across different settings and assumptions
• Federated learning process involves iterative rounds of local training, model aggregation, and model update
  1. Local training each device (client) trains the model on its local data for a few epochs
  2. Model aggregation local model updates (gradients) are sent to a central server (coordinator) for aggregation $w_{t+1} = \sum_{k=1}^{K} \frac{n_k}{n} w_{t+1}^k$
  3. Model update the aggregated model is distributed back to the devices for the next round of local training
• Privacy-preserving techniques protect sensitive information during federated learning
  • Secure aggregation encrypts local model updates before sending them to the server preventing the server from accessing individual updates
  • Differential privacy adds noise (Laplacian, Gaussian) to the model updates to protect individual device data from being inferred
  • Homomorphic encryption enables computation (addition, multiplication) on encrypted data without decrypting it first

Centralized vs decentralized ML in IoT

• Centralized machine learning relies on a central server (cloud) for model training and inference
  • Advantages
    • Easier to manage and update models since they are stored and served from a central location
    • Access to the entire dataset for training potentially leading to higher model accuracy and generalization
  • Disadvantages
    • Privacy concerns sensitive data (user behavior, personal information) is sent to a central server increasing the risk of data breaches and misuse
    • Communication overhead large amounts of data need to be transferred to the cloud consuming network bandwidth and incurring latency
    • Single point of failure the centralized server becomes a bottleneck and a potential point of failure disrupting the entire system if unavailable
• Decentralized machine learning (federated learning) distributes model training and inference across multiple devices (edge, IoT)
  • Advantages
    • Improved privacy raw data remains on the devices only model updates are shared reducing the risk of data leakage and protecting user privacy
    • Reduced communication overhead only model parameters (kilobytes) are exchanged not the entire dataset (gigabytes) making it efficient for bandwidth-constrained networks
    • Increased robustness no single point of failure the system can continue to operate even if some devices are offline or disconnected
  • Disadvantages
    • Increased complexity coordinating model training across multiple devices with different capabilities (processing power, memory) and network conditions (latency, bandwidth) can be challenging
    • Potential for slower convergence model updates from devices may be delayed or asynchronous leading to slower convergence compared to centralized training
    • Model inconsistency devices may have different data distributions (user preferences, environmental factors) leading to varying model performance across devices and potential fairness issues