  Bilevel optimization has been successfully applied to many important machine
learning problems. Algorithms for solving bilevel optimization have been
studied under various settings. In this paper, we study the
nonconvex-strongly-convex bilevel optimization under a decentralized setting.
We design decentralized algorithms for both deterministic and stochastic
bilevel optimization problems. Moreover, we analyze the convergence rates of
the proposed algorithms in difference scenarios including the case where data
heterogeneity is observed across agents. Numerical experiments on both
synthetic and real data demonstrate that the proposed methods are efficient.
