The chapter describes recent developments in distributed processing over adaptive networks. The resulting adaptive learning rules rely on local data at the individual nodes and on collaborations among neighboring nodes in order to exploit the space-time dimension of the data more fully. The ideas are illustrated by considering algorithms of the least mean-squares type, although more general adaptation rules are also possible including least-squares rules and Kalman-type rules. Both incremental and diffusion collaboration strategies are considered