Open Access

ARTICLE

DSSeg-FLHA: A Decentralized Secure Self-Adapting Image Segmentation Framework Using Federated Learning and Hybrid Architectures

Rifat Sarker Aoyon¹, Fahmid Al Farid^2,3, Ismail Hossain⁴, Mahe Zabin⁵, Sarina Mansor^2,*, Jia Uddin^6,*

1 Department of Computer Engineering, Chosun University, Gwangju, Republic of Korea
2 Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Malaysia
3 Faculty of Computer Science and Informatics, Berlin School of Business and Innovation, Berlin, Germany
4 Department of Computer Science, University of Denver, Denver, CO, USA
5 Human and Digital Interface Department, JW Kim College of Future Studies, Woosong University, Daejeon, Republic of Korea
6 AI and Big Data Department, Woosong University, Daejeon, Republic of Korea

* Corresponding Authors: Sarina Mansor. Email: ; Jia Uddin. Email:

Computers, Materials & Continua 2026, 88(2), 75 https://doi.org/10.32604/cmc.2026.079831

Received 29 January 2026; Accepted 14 April 2026; Issue published 15 June 2026

Abstract

This research introduces an innovative lightweight image segmentation framework where models of hybrid architectures work together to predict the output and also have self-adapting ability, along with maintaining data privacy. In this framework, data is distributed and trained in a decentralized way using different deep learning architectures. That is how the advantages of all these models will be integrated into the system. Each trained model makes its own prediction, and the final output is determined through cooperation among these models. Here, the confidence-level and pixel-wise voting majority algorithms will be utilized for the co-operation-based output prediction system. Due to the efficient setup of the operations of these two algorithms, each input will get its accurate output. Additionally, the federated learning-based self-adapting feature facilitated the proposed framework for advancing its performance consistently by interacting with the inputs. Here, UNet, SegNet and FCNN models have been trained and integrated into the prediction framework. Here, the Oxford-IIT pet dataset was used. And all the data of this dataset is distributed among these three models. The framework’s effectiveness was measured using metrics like average pixel accuracy, IoU, F1 score, precision, and recall, which resulted in scores of 89.26%, 71.48%, 81.29%, 83.96% and 81.16%, respectively. Another notable feature of this proposed framework is allocating comparatively fewer computational resources and taking less time. To validate these claims, the proposed system is compared with three other state-of-the-art models, and the proposed system delivered superior performance among all.

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Keywords

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