Meta-heuristic as manager in federated learning approaches for image processing purposes
Open accessAbstract
The new form of artificial intelligence training, i.e. federated learning, is becoming more popular in the last few years. It is an optimization problem that includes additional mechanisms such as aggregation and data transmission. In this paper, we propose a hybridization of this type of training with a meta-heuristic. The meta-heuristic algorithm is adapted to manage the entire process as well as to analyze the best models to minimize attacks on this type of collaboration. The proposed solution is based on minimizing the general model error, with additional control mechanisms for incoming models, or adapting the aggregation method depending on the quality of the model. The innovative solution has been analyzed in terms of its application to the problem of image classification using classical and convolutional neural networks, and the most popular meta-heuristic algorithms. The proposal was analyzed in terms of the accuracy of the general model as well as for security against poisoning attacks. We reached 91% of accuracy using the proposed method with the Red Fox Optimization Algorithm and 95% in terms of detection of poisoned samples in the database.
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Polap and their team published three research papers [17], [19], [22], all focused on skin cancer detection with FL.
“Researcher Polap and their team have published three research papers [17], [19], [22], all of them focused on skin cancer detection with the FL environment.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
Six reviewed papers [17], [19], [22], [23], [29], [30] used more than 10,000 images individually.
“Finally, we found six papers [17], [19], [22], [23], [29], [30] all of them have used more than 10,000 images individually.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
Połap and the groups used the MNIST: HAM10000 dataset in a series of articles [17], [19], [22] with FL environment.
“Połap and the groups used the dataset in a series of articles [17], [19], [22] with FL environment.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
Inception was used in [17], [19], and [22]; AlexNet in [17] and [19].
“Other pre-trained CNN models are Inception ( [17], [19], [22]), AlexNet ( [17], [19]).”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
Połap and Woźniak [19] proposed a meta-heuristic search based federated model that calculated average loss of all local models and selected only models scoring higher than average loss for server aggregation.
“Połap and Woźniak [19] proposed a meta-heuristic search based federated model, first they calculated average loss of all local models and then selected only models that have scored higher than the average loss for aggregation in server.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
Połap and Woźniak [19] used the inception91 classifier for the FL model and obtained an accuracy of 91%.
“Połap and Woźniak [19] used the inception91 classifier for the FL model and obtained an accuracy of 91%.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
In Połap and Woźniak [19], non-IID data is not considered.
“Non-IID data is not considered.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
In Połap and Woźniak [19], result comparison with state-of-the-art is given.
“result comparison with state-of-”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
In Połap and Woźniak [19], the proposed FL framework is well described.
“The proposed FL framework is well de-”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [19]
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Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm
Ketafa, F. H., & Al-Darraji, S. (2024) · Iraqi Journal of Science · doi:10.24996/ijs.2024.65.2.38 · Cited by 3 · see citing works
Enhancing natural disaster analysis and waste classification: a novel VGG-FL approach
Soundararajan, S., Josphineleela, R., Bisht, A. K., & Saravanan, D. (2024) · Environmental Monitoring and Assessment · doi:10.1007/s10661-024-12745-5 · Cited by 2 · see citing works
DCNFYOLO: Dual-Convolution Network and Feature Fusion for High-Precision Smoke Detection
Chen, X., Liu, X. L., Liu, B., & Zhu, Y. (2024) · Electronics · doi:10.3390/electronics13193864 · Cited by 2 · see citing works
Mayfly-based federated learning approach for early detection of fire
Salam, M. A., Badr, E. M., & Elbohoty, A. A. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00876-y · Cited by 2 · see citing works
Shahzad, K., Khashir, A., Tufail, H., Ahad, A., Ali, Z., Madeira, F., & Pires, I. M. (2025) · Array · doi:10.1016/j.array.2025.100667 · Cited by 2 · see citing works
Firdaus, N., & Raza, Z. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02875-8 · Cited by 2 · see citing works
Cluster knowledge-driven vertical federated learning
Yin, Z., Zhao, X., Wang, H., Zhang, X., Guo, X., & Fang, Z. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06232-4 · Cited by 2 · see citing works
An Integrated Federated Learning and Meta-Learning Approach for Mining Operations
Munagala, V. S. K. R., Singh, S., Thudumu, S., Logothetis, I., Bhandari, S., Bhandari, A., Mouzakis, K., & Vasa, R. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8388-9_31 · Cited by 1 · see citing works
Study on the Twoing Criterion with Pre-pruning and Bagging Method for Dispersed Data
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Engineering Brain Technology Based on Artificial Intelligence and Federated Learning
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Analysis of Enhanced Ant Lion Optimizer Algorithms for Robust Dimensionality Reduction
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Adaptive Fairness Federal LearningPreprint
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Metaheuristic-Based Neuroevolution Framework for Improved Pneumonia Classification in X-ray Images
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Image Detection Using Federated Learning
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- Authors
- Połap, D., & Woźniak, M.
- Affiliations
- Silesian University of Technology (PL)
- Corresponding author
- Połap, Dawid
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 113
- Pages
- 107872
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W3198908542
- Cited by
- 65
- References
- 52
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 13 · 2023: 17 · 2022: 20 · 2021: 1
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Soft Computing (publishedVersion, cc-by)
- Funders
- Silesian University of Technology (BKM-512/RMS2/2021)
- Narodowa Agencja Wymiany Akademickiej (PPI/PRO/2019/1/00051)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- federated learning, metaheuristic optimization, model aggregation, poisoning attacks, image classification, convolutional neural networks, model selection, poisoning detection
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