  Distributed adaptive networks achieve better estimation performance by
exploiting temporal and as well spatial diversity while consuming few
resources. Recent works have studied the single task distributed estimation
problem, in which the nodes estimate a single optimum parameter vector
collaboratively. However, there are many important applications where the
multiple vectors have to estimated simultaneously, in a collaborative manner.
This paper presents multi-task diffusion strategies based on the Affine
Projection Algorithm (APA), usage of APA makes the algorithm robust against the
correlated input. The performance analysis of the proposed multi-task diffusion
APA algorithm is studied in mean and mean square sense. And also a modified
multi-task diffusion strategy is proposed that improves the performance in
terms of convergence rate and steady state EMSE as well. Simulations are
conducted to verify the analytical results.
