HFL-Lite: Sparse Coding for Lightweight Hierarchical Federated Learning in Computation-Limited Sensor Networks

Abstract

The rapid growth of sensor-equipped edge devices calls for machine learning paradigms that are both lightweight and privacy-preserving. Federated learning (FL) enables distributed AI on edge data but still demands heavy forward and backward propagation on resource-constrained sensors. Split learning (SL) offloads computation to a server, yet its intermediate activations can leak private information. Homomorphic encryption closes this gap at the cost of prohibitive computation and bloated communication. We propose HFL-Lite, a hierarchical federated learning framework that achieves practical privacy without cryptographic primitives. Each sensor encodes its raw data into a compact sparse feature code via a lightweight sensor encoder. Only this code, which is insufficient to reconstruct the original data, leaves the device. An edge server synthesizes surrogate training samples from the uploaded codes, trains a teacher model, and distills knowledge back to the sensors. Because the sensors perform only one-way uploads, backward propagation, gradient transmission, and plaintext activation leakage are all eliminated. Experiments on the UCI HAR dataset show that HFL-Lite reduces per-round communication to 0.5 KB and sensor-side latency to 9 ms while delivering competitive accuracy.