Frequency-Domain Distributed Multitask Learning Over Networks

Distributed multitask learning, which aims to estimate multiple parameter vectors in a distributed and collaborative manner, has been applied to a wide range of Internet of Things (IoTs). However, in many practical applications, such as channel impulse responses (CIRs) estimation and adaptive line enhancer (ALE), the parameter vectors of interest can be of large size, resulting in high computational cost. Frequency-domain (FD) learning that utilizes efficient Fast Fourier Transform (FFT) may be a good candidate to reduce computational complexity. Moreover, benefited from the pre-whitening effect caused by the orthogonality property of FFT, the estimation performance can be improved. However, existing research on FD learning mainly focuses on single-task scenarios, which is not applicable to multitask scenarios. Considering this, in this article, we first formulate the FD distributed multitask learning problem, and then propose a frequency-domain distributed multitask learning algorithm. Through performing numerical simulations and practical applications to CIRs estimation and smart grid state estimation, it is observed that the proposed algorithm exhibits lower computational complexity and better estimation performance, compared with the existing time-domain distributed algorithms. IEEE