We address for the first time the question of how networked agents can collaboratively fit a Morozov-regularized linear model when each agent knows a summand of the regression data. This question generalizes previously studied data-splitting scenarios, which require that the data be partitioned among the agents. To answer the question, we introduce a class of network-structured problems, which contains the regularization problem, and by using the Douglas-Rachford splitting algorithm, we develop a distributed algorithm to solve these problems. We illustrate through simulations that our approach is an effective strategy for fully distributed linear regression.
['Francois D. Cote', 'Ioannis N. Psaromiligkos', 'Warren J. Gross']
In-network linear regression with arbitrarily split data matrices