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.

此研究提出了一种被称为 EdgeConvEns 的卷积集成学习方法，它在边缘设备上训练异构弱模型，并学习将它们集成起来，从而提高整体的预测性能。通过在边缘设备上独立实现和训练 Field-Programmable Gate Array（FPGA）设备上的模型，并将学习到的数据表示传输到中央服务器，EdgeConvEns 能够在多种训练场景下更少的通信和数据使用的情况下，胜过当前最先进的性能。