  With rising powerful, low-cost embedded devices, the edge computing has
become an increasingly popular choice. In this paper, we propose a new
incremental local stochastic gradient descent (SGD) tailored on the Raspberry
Pi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with
1,000 classes. The local SGD splits the data block into $k$ partitions using
$k$means algorithm and then it learns in the parallel way SGD models in each
data partition to classify the data locally. The incremental local SGD
sequentially loads small data blocks of the training dataset to learn local SGD
models. The numerical test results on Imagenet dataset show that our
incremental local SGD algorithm with the Raspberry Pi 4 is faster and more
accurate than the state-of-the-art linear SVM run on a PC Intel(R) Core i7-4790
CPU, 3.6 GHz, 4 cores.
