  This article proposes a distributed multi-task learning (MTL) algorithm based
on supervised principal component analysis (SPCA) which is: (i) theoretically
optimal for Gaussian mixtures, (ii) computationally cheap and scalable.
Supporting experiments on synthetic and real benchmark data demonstrate that
significant energy gains can be obtained with no performance loss.
