Federated learning is gaining traction in IoT environments where data privacy and bandwidth are critical. This article presents a practical implementation of local model training on edge devices, followed by secure aggregation. It addresses key challenges such as heterogeneous hardware, intermittent connectivity, and non-IID data distributions. The approach demonstrates how to balance model accuracy with communication efficiency, making it a valuable reference for engineers working on distributed AI systems. The techniques discussed can be adapted to various IoT scenarios, from smart homes to industrial sensors.
Practical insights into deploying federated learning on IoT edge nodes, covering local training and aggregation.