  The design of decentralized learning algorithms is important in the
fast-growing world in which data are distributed over participants with limited
local computation resources and communication. In this direction, we propose an
online algorithm minimizing non-convex loss functions aggregated from
individual data/models distributed over a network. We provide the theoretical
performance guarantee of our algorithm and demonstrate its utility on a real
life smart building.
