In this paper, for the first time, we combine the assumption of partial data exchange over
adaptive networks with the quantization-aware diffusion least mean square (DQA-LMS) to …

Modern data analysis and processing tasks usually involve large sets of data structured by a
graph. Typical examples include brain activity supported by neurons, data shared by users …

Multitask distributed optimization over networks enables the agents to cooperate locally to
estimate multiple related parameter vectors. In this work, we consider multitask estimation …

Multitask learning has received considerable attention in signal processing and machine
learning communities. It aims at simultaneously learning several related tasks other than the …

This paper aims to establish a distributed method for topology inference from streaming
graph signals. The measurements are acquired by agents and used to estimate meaningful …

In adaptive diffusion networks, one of the main challenges is the large volume of data
exchange among nodes needed to arrive at a collective decision. In this study, a new model …