Machine learning has many challenges, and one of them is to deal with large datasets, because the size of them grows continuously year by year. One solution to this problem is data parallelism. This paper investigates the expansion of data parallelism to mobile, which became the most popular platform. Special client-server architecture was created for this purpose. The software implementation of this problem measures the mobile devices training capabilities and the efficiency of the whole system. The results show that doing distributed training on mobile cluster is possible and safe, but its performance depends on the algorithm's implementation.
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