We study the problem of distributed estimation, where a set of nodes are required to collectively estimate some parameter of interest from their measurements. Diffusion algorithms have been shown to achieve good performance, increased robustness and are amenable for real-time implementations. In this work we focus on multi-level diffusion algorithms, where a network running a diffusion algorithm is enhanced by adding special nodes that can perform different processing. These special nodes form a second network where a second diffusion algorithm is implemented. We illustrate the concept using diffusion LMS, provide performance analysis for multi-level collaboration and present simulation results showing improved performance over conventional diffusion.