Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva. Adaptive diffusion networks: An overview. Signal Processing, v.223, pp.1-27, 2024.

Abstract

This work provides a comprehensive overview of adaptive diffusion networks, from the first papers published on the subject to state-of-the-art solutions and current challenges. These networks consist of a collection of agents that can measure and learn from streaming data locally, and cooperate to improve the overall performance. Since their inception, adaptive diffusion networks have consolidated themselves as interesting tools for distributed estimation and learning, and have spun several types of solutions for these problems. We begin by discussing the technological advances that led to their emergence, and present the many ramifications of the area. We also discuss some of the most critical limitations of these types of networks in practical situations, such as energy consumption, and show techniques that have been proposed to cope with them. Finally, simulations with real-world data are presented in order to illustrate in practice the opportunities and challenges that they pose.

Keywords

Diffusion networks, Distributed estimation, Distributed signal processing, Multitask learning, Kernel adaptive filtering, Graph signal processing.

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