  Distributed machine learning is becoming a popular model-training method due
to privacy, computational scalability, and bandwidth capacities. In this work,
we explore scalable distributed-training versions of two algorithms commonly
used in object detection. A novel distributed training algorithm using Mean
Weight Matrix Aggregation (MWMA) is proposed for Linear Support Vector Machine
(L-SVM) object detection based in Histogram of Orientated Gradients (HOG). In
addition, a novel Weighted Bin Aggregation (WBA) algorithm is proposed for
distributed training of Ensemble of Regression Trees (ERT) landmark
localization. Both algorithms do not restrict the location of model aggregation
and allow custom architectures for model distribution. For this work, a
Pool-Based Local Training and Aggregation (PBLTA) architecture for both
algorithms is explored. The application of both algorithms in the medical field
is examined using a paradigm from the fields of psychology and neuroscience -
eyeblink conditioning with infants - where models need to be trained on facial
images while protecting participant privacy. Using distributed learning, models
can be trained without sending image data to other nodes. The custom software
has been made available for public use on GitHub:
https://github.com/SLWZwaard/DMT. Results show that the aggregation of models
for the HOG algorithm using MWMA not only preserves the accuracy of the model
but also allows for distributed learning with an accuracy increase of 0.9%
compared with traditional learning. Furthermore, WBA allows for ERT model
aggregation with an accuracy increase of 8% when compared to single-node
models.
