Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 199-208

Original Article

Enhanced Hybrid Model in Federated Learning Environment for Medical Heterogeneous Images

Tiba Saad Mohammed 1 Hilal Al-Libawy 1
1 Department of Electrical Engineering, College of Engineering, University of Babylon, Babel, Iraq
History
  • Received: March 4, 2024
  • Revised: April 25, 2024
  • Accepted: May 24, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.18

Abstract

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 system without sharing their data. The training process is kept local to protect the privacy of the data. Among the many difficulties 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 in the model’s predictions and other undesirable outcomes. This paper discussed ways to solve this problem where clients with heterogeneous data were dealt with in terms of number and type. Because there are different types of image data through 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 of this disease by using two clients, each with one of these different data. Good results were obtained with an accuracy of more than 99% in one customer and more than 95% in the second client while maintaining the privacy of this data.

Keywords

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