The multitask diffusion LMS is an efficient strategy to simultaneously infer,
in a collaborative manner, multiple parameter vectors. Existing works on
multitask problems assume that all agents respond to data synchronously. In
several applications, agents may not be able to act synchronously because
networks can be subject to several sources of uncertainties such as changing
topology, random link failures, or agents turning on and off for energy
conservation. In this work, we describe a model for the solution of multitask
problems over asynchronous networks and carry out a detailed mean and
mean-square error analysis. Results show that sufficiently small step-sizes can
still ensure both stability and performance. Simulations and illustrative
examples are provided to verify the theoretical findings. The framework is
applied to a particular application involving spectral sensing.

本文提供了一种解决异步网络上多任务问题的模型，它采用多任务扩散 LMS 算法以协作的方式同时推断多个参数向量，并描述了详细的均方误差分析，该算法在充足步长的条件下保证稳定和性能，并将其应用于光谱感知的特定应用中。