  In recent years, storing large volumes of data on distributed devices has
become commonplace. Applications involving sensors, for example, capture data
in different modalities including image, video, audio, GPS and others. Novel
algorithms are required to learn from this rich distributed data. In this
paper, we present consensus based multi-layer perceptrons for
resource-constrained devices. Assuming nodes (devices) in the distributed
system are arranged in a graph and contain vertically partitioned data, the
goal is to learn a global function that minimizes the loss. Each node learns a
feed-forward multi-layer perceptron and obtains a loss on data stored locally.
It then gossips with a neighbor, chosen uniformly at random, and exchanges
information about the loss. The updated loss is used to run a back propagation
algorithm and adjust weights appropriately. This method enables nodes to learn
the global function without exchange of data in the network. Empirical results
reveal that the consensus algorithm converges to the centralized model and has
performance comparable to centralized multi-layer perceptrons and tree-based
algorithms including random forests and gradient boosted decision trees.
