Federated learning based multi-head attention framework for medical image classification

Abstract

In this study, we propose a novel Federated Learning Based Multi-Head Attention (FBMA) framework for image classification problems considering the Independent and Identically Distributed (IID) and Non-Independent and Identically Distributed (Non-IID) medical data. The FBMA architecture integrates FL principles with the Multi-Head Attention mechanism, optimizing the model performance and ensuring privacy. Using Multi-Head Attention, the FBMA framework allows the model to selectively focus on important regions of the image for feature extraction, and using FL, FBMA leverages decentralized medical institutions to facilitate collaborative model training while maintaining data privacy. Through rigorous experimentation on medical image datasets: MedMNIST Dataset, MedicalMNIST Dataset, and LC25000 Dataset, each partitioned into Non-IID data distribution, the proposed FBMA framework exhibits high-performance metrics. The results highlight the efficacy of our proposed FBMA framework, indicating its potential for real-world applications where image classification demands both high accuracy and data privacy.