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.
Aoyon et al. (2026) studied this question.