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

Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning