Emerging edge intelligence applications require the server to retrain and
update deep neural networks deployed on remote edge nodes to leverage newly
collected data samples. Unfortunately, it may be impossible in practice to
continuously send fully updated weights to these edge nodes due to the highly
constrained communication resource. In this paper, we propose the weight-wise
deep partial updating paradigm, which smartly selects a small subset of weights
to update in each server-to-edge communication round, while achieving a similar
performance compared to full updating. Our method is established through
analytically upper-bounding the loss difference between partial updating and
full updating, and only updates the weights which make the largest
contributions to the upper bound. Extensive experimental results demonstrate
the efficacy of our partial updating methodology which achieves a high
inference accuracy while updating a rather small number of weights.

本文提出了一种新的权重部分更新方法，通过分析损失差异上限来智能选择每个服务器与远程边缘节点通信轮次中要更新的少量权重，从而实现与完全更新相似的性能，可应用于新兴的边缘智能应用。实验结果表明，在更新数量较少的情况下实现了高的推理准确性。