Enhanced Hybrid Model in Federated Learning Environment for Medical Heterogeneous Images - Saad Mohammed, Tiba; Al-Libawy, Hilal (Iraqi Journal for Electrical and Electronic Engineering, 2026)
Federated learning (FL) is one of the newest and most significant fields for developing artificial intelligence applications.This technology trains its models in a distributed way, using data from different clients who work together in the systemwithout sharing their data. The training process is kept local to protect the privacy of the data. Among the manydifficulties that have arisen due to the novelty of this technology is the issue of heterogeneous data between typical clients.Client’s data may differ from each other in different respects, for example non identically and independent distribution(non-IID) between clients and the difference in the type of data used in each client. This can lead to inconsistencies inthe model’s predictions and other undesirable outcomes. This paper discussed ways to solve this problem where clientswith heterogeneous data were dealt with in terms of number and type. Because there are different types of image datathrough which doctors can diagnose coronavirus, such as x-rays, CT-scan. A hybrid convolution neural network (CNN )and long short-term memory model (LSTM) has been proposed in a federated learning system to predict the incidence ofthis disease by using two clients, each with one of these different data. Good results were obtained with an accuracy ofmore than 99% in one customer and more than 95% in the second client while maintaining the privacy of this data.