Federated Optimization: Distributed Optimization Beyond the Datacenter

We introduce a new and increasingly relevant setting for distributed optimization in machine learning,
where the data defining the optimization are distributed (unevenly) over an extremely large
number of nodes, but the goal remains to train a high-quality centralized model. We refer to this setting
as Federated Optimization. In this setting, communication efficiency is of utmost importance.
A motivating example for federated optimization arises when we keep the training data locally on
users’ mobile devices rather than logging it to a data center for training. Instead, the mobile devices
are used as nodes performing computation on their local data in order to update a global model. We
suppose that we have an extremely large number of devices in our network, each of which has only
a tiny fraction of data available totally; in particular, we expect the number of data points available
locally to be much smaller than the number of devices. Additionally, since different users generate
data with different patterns, we assume that no device has a representative sample of the overall
distribution.
We show that existing algorithms are not suitable for this setting, and propose a new algorithm which
shows encouraging experimental results. This work also sets a path for future research needed in the
context of federated optimization.

Source: http://arxiv.org/pdf/1511.03575v1.pdf