  This chapter deals with decentralized learning algorithms for in-network
processing of graph-valued data. A generic learning problem is formulated and
recast into a separable form, which is iteratively minimized using the
alternating-direction method of multipliers (ADMM) so as to gain the desired
degree of parallelization. Without exchanging elements from the distributed
training sets and keeping inter-node communications at affordable levels, the
local (per-node) learners consent to the desired quantity inferred globally,
meaning the one obtained if the entire training data set were centrally
available. Impact of the decentralized learning framework to contemporary
wireless communications and networking tasks is illustrated through case
studies including target tracking using wireless sensor networks, unveiling
Internet traffic anomalies, power system state estimation, as well as spectrum
cartography for wireless cognitive radio networks.
