Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data

TL;DR

This paper proposes a new federated learning aggregation method that improves convergence in medical image classification tasks, addressing privacy constraints and data distribution challenges.

Contribution

A novel aggregation strategy for federated learning that enhances convergence speed and performance in medical imaging applications.

Findings

Improved convergence over traditional aggregation methods.

Effective in cerebral MRI image classification.

Addresses data privacy and distribution issues.

Abstract

The development of deep learning techniques is a leading field applied to cases in which medical data is used, particularly in cases of image diagnosis. This type of data has privacy and legal restrictions that in many cases prevent it from being processed from central servers. However, in this area collaboration between different research centers, in order to create models as robust as possible, trained with the largest quantity and diversity of data available, is a critical point to be taken into account. In this sense, the application of privacy aware distributed architectures, such as federated learning arises. When applying this type of architecture, the server aggregates the different local models trained with the data of each data owner to build a global model. This point is critical and therefore it is fundamental to analyze different ways of aggregation according to the use case, taking into account the distribution of the clients, the characteristics of the model, etc. In this paper we propose a novel aggregation strategy and we apply it to a use case of cerebral magnetic resonance image classification. In this use case the aggregation function proposed manages to improve the convergence obtained over the rounds of the federated learning process in relation to different aggregation strategies classically implemented and applied.