EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence

TL;DR

EdgeConvEns introduces a convolutional ensemble learning framework for edge intelligence that trains heterogeneous models on edge devices and ensembles their learned features centrally, reducing communication and computational demands.

Contribution

It proposes a novel decentralized ensemble learning method that trains weak, heterogeneous models on edge devices and combines their features centrally, improving performance with less communication.

Findings

Outperforms state-of-the-art methods in accuracy.

Requires fewer communication rounds.

Operates efficiently on FPGA edge devices.

Abstract

Deep edge intelligence aims to deploy deep learning models that demand computationally expensive training in the edge network with limited computational power. Moreover, many deep edge intelligence applications require handling distributed data that cannot be transferred to a central server due to privacy concerns. Decentralized learning methods, such as federated learning, offer solutions where models are learned collectively by exchanging learned weights. However, they often require complex models that edge devices may not handle and multiple rounds of network communication to achieve state-of-the-art performances. This study proposes a convolutional ensemble learning approach, coined EdgeConvEns, that facilitates training heterogeneous weak models on edge and learning to ensemble them where data on edge are heterogeneously distributed. Edge models are implemented and trained independently on Field-Programmable Gate Array (FPGA) devices with various computational capacities. Learned data representations are transferred to a central server where the ensemble model is trained with the learned features received from the edge devices to boost the overall prediction performance. Extensive experiments demonstrate that the EdgeConvEns can outperform the state-of-the-art performance with fewer communications and less data in various training scenarios.