  We introduce CoLN, Combined Learning of Neural network weights, a novel
method to securely combine Machine Learning models over sensitive data with no
sharing of data. With CoLN, local hosts use the same Neural Network
architecture and base parameters to train a model using only locally available
data. Locally trained models are then submitted to a combining agent, which
produces a combined model. The new model's parameters can be sent back to
hosts, and can then be used as initial parameters for a new training iteration.
CoLN is capable of combining several distributed neural networks of the same
kind but is not restricted to any single neural architecture. In this paper we
detail the combination algorithm and present experiments with feed-forward,
convolutional, and recurrent Neural Network architectures, showing that the
CoLN combined model approximates the performance of a hypothetical ideal
centralized model, trained using the combination of the local datasets. CoLN
can contribute to secure collaborative research, as required in the medical
area, where privacy issues preclude data sharing, but where the limitations of
local data demand information derived from larger datasets.
