Federated learning on edge devices requires hardware that balances computational power, energy efficiency, and connectivity. The core components include a processing unit capable of running machine learning models, sufficient memory to handle training tasks, and reliable networking hardware for communication with a central server. Edge devices range from smartphones and IoT sensors to industrial controllers, so hardware specifics depend on the use case. However, common requirements include a CPU or specialized accelerator (like a GPU or NPU), RAM for temporary data storage, and wireless modules (Wi-Fi, cellular, or Bluetooth) for transmitting model updates.
First, the processing unit must handle local model training and inference. For basic tasks, a modern multi-core CPU (e.g., ARM Cortex-A series in smartphones) may suffice, but more complex models benefit from accelerators like Google’s Edge TPU or NVIDIA’s Jetson modules. For example, a Raspberry Pi 4 with a quad-core Cortex-A72 CPU can train lightweight models like logistic regression or small neural networks. Devices with limited compute, such as microcontrollers (e.g., ESP32), may only support tinyML frameworks like TensorFlow Lite Micro, which require model optimization techniques like quantization. The choice depends on the model’s complexity and latency requirements—real-time applications like voice recognition often demand dedicated AI chips.
Second, memory and storage are critical for caching training data and model parameters. Edge devices typically need at least 1-2GB of RAM to manage intermediate computations, though resource-constrained devices (e.g., Arduino Uno) might rely on external flash storage. For instance, training a federated vision model on a smartphone requires storing batches of images temporarily in RAM. Storage must also retain the model architecture and updates between communication rounds. Devices like industrial cameras might use embedded eMMC storage (32-64GB) to handle larger datasets. However, federated learning frameworks often minimize memory usage by design—tools like PySyft or Flower optimize data sharding and gradient aggregation to reduce overhead.
Finally, connectivity hardware ensures timely communication with the central server. Devices need stable, low-latency connections to send model updates (e.g., gradients) and receive global model parameters. Wi-Fi modules (e.g., Broadcom/Cypress chips) are common in smart home devices, while cellular modems (e.g., Quectel modules) suit remote sensors. Bandwidth requirements depend on update size: a federated NLP model with 10MB parameters might need 5-10MB per update cycle. Security is also a consideration—hardware-backed encryption (e.g., TPM chips in industrial gateways) protects data in transit. In summary, federated learning on edge devices demands a balance of compute, memory, and connectivity tailored to the application’s scale and constraints.