Energy Efficient Federated Learning with Hyperdimensional Computing (HDC)

TL;DR

This paper presents an energy-efficient federated learning framework using hyperdimensional computing and differential privacy, optimizing resource parameters to significantly reduce energy consumption while maintaining accuracy.

Contribution

It introduces a novel FL framework combining HDC and DP, with a hybrid optimization algorithm for resource management, achieving substantial energy savings.

Findings

Up to 83.3% energy reduction compared to baselines

Maintains high accuracy and faster convergence

Effective joint optimization of HDC dimension, transmit power, and CPU frequency

Abstract

This paper investigates the problem of minimizing total energy consumption for secure federated learning (FL) in wireless edge networks, a key paradigm for decentralized big data analytics. To tackle the high computational cost and privacy challenges of processing large-scale distributed data with conventional neural networks, we propose an FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. Each edge device employs hyperdimensional computing (HDC) for lightweight local training and applies differential privacy (DP) noise to protect transmitted model updates. The total energy consumption is minimized through a joint optimization of the HDC dimension, transmit power, and CPU frequency. An efficient hybrid algorithm is developed, combining an outer enumeration search for HDC dimensions with an inner one-dimensional search for resource allocation. Simulation results show that the proposed framework achieves up to 83.3% energy reduction compared with baseline schemes, while maintaining high accuracy and faster convergence.