# Affine Combination of Diffusion Strategies over Networks

arXiv: https://arxiv.org/abs/2002.03209 · HTML: https://mlsanity.com/paper/2002.03209/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment](https://mlsanity.com/paper/2305.06124/) (2023)

## Abstract (arXiv)

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strategies for enhanced performance. By assigning a combination coefficient to each node, and using an adaptation mechanism to minimize the network error, we obtain a combined diffusion strategy that benefits from the best characteristics of all component strategies simultaneously in terms of excess-mean-square error (EMSE). Analyses of the universality are provided to show the superior performance of affine combination scheme and to characterize its behavior in the mean and mean-square sense. Simulation results are presented to demonstrate the effectiveness of the proposed strategies, as well as the accuracy of theoretical findings.