FL-HDC: Hyperdimensional Computing Design for the Application of Federated Learning
Cheng-Yen Hsieh, Yu-Chuan Chuang, An-Yeu Wu
Abstract
Federated learning (FL) is a privacy-preserving learning framework, which collaboratively learns a centralized model across edge devices. Each device trains an independent model with its local dataset and only uploads model parameters to mitigate privacy concerns. However, most FL works focus on deep neural networks (DNNs), whose intensive computation hinders FL from practical realization on resource-limited edge devices. In this paper, we exploit the high energy efficiency properties of hyperdimensional computing (HDC) to propose a federated learning HDC (FL-HDC). In FL-HDC, we bipolarize model parameters to significantly reduce communication costs, which is a primary concern in FL. Moreover, we propose a retraining mechanism with adaptive learning rates to compensate for the accuracy degradation caused by bipolarization. Under the FL scenario, our simulation results show the effectiveness of our proposed FL-HDC across two datasets, MNIST and ISOLET. Compared with the previous work that transmits complete model parameters to the cloud, FL-HDC greatly reduces 23x and 9x communication costs with comparable accuracy in ISOLET and MNIST, respectively.