EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence
Ilkay Sikdokur İnci M. Baytaş Arda Yurdakul
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.