# Multitask diffusion adaptation over networks with common latent representations

## TL;DR

This paper introduces a novel distributed online multitask learning framework where multiple tasks share a common latent feature representation, enabling efficient collaboration among agents in streaming data environments.

## Contribution

It proposes a new multitask learning model based on shared latent representations and develops distributed algorithms with a unified mean-square-error analysis.

## Key findings

- Algorithms demonstrate effective multitask learning with shared features.
- Theoretical analysis confirms stability and convergence properties.
- Simulations validate the approach's potential in practical applications.

## Abstract

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In single-task adaptation, agents cooperate to track an objective of common interest, while in multitask adaptation agents track multiple objectives simultaneously. Regularization is one useful technique to promote and exploit similarity among tasks in the latter scenario. This work examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation. As a result, a new multitask learning formulation is presented and algorithms are developed for its solution in a distributed online manner. We present a unified framework to analyze the mean-square-error performance of the adaptive strategies, and conduct simulations to illustrate the theoretical findings and potential applications.