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{'authors': [{'author_name': 'Rodrigues',
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              'uuid': '11e49250-4853-48ba-aaca-725aed5a09de'},
             {'author_name': 'Leonardo Gabriel Ferreira and Rodrigues Moreira',
              'display_name': 'Leonardo Gabriel Ferreira and Rodrigues Moreira',
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              'publication_uuid': '907bd5bf-ebb1-42ae-b260-fb0745a46aa6',
              'uuid': '65d4999f-bb64-44f1-ab75-f35280d2236e'},
             {'author_name': 'Larissa Ferreira and Moreira',
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              'publication_uuid': '907bd5bf-ebb1-42ae-b260-fb0745a46aa6',
              'uuid': '576ddc7a-30de-42fc-921b-c4c41f319ad1'},
             {'author_name': 'Rodrigo and Backes',
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              'publication_uuid': '907bd5bf-ebb1-42ae-b260-fb0745a46aa6',
              'uuid': 'f6a62fe4-955c-442a-a132-2a9fdd82df85'},
             {'author_name': 'André Ricardo',
              'display_name': 'André Ricardo',
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              'publication_uuid': '907bd5bf-ebb1-42ae-b260-fb0745a46aa6',
              'uuid': '3448e3b2-d64f-4140-b021-466ddb29f39b'}],
 'bibtex': '@article{Rodrigues_Rodrigues Moreira_Moreira_Backes_2026, '
           'title={PSO-based Client Selection to Improve Medical Image '
           'Classification in Federated Learning}, volume={33}, '
           'url={https://seer.ufrgs.br/index.php/rita/article/view/150621}, '
           'DOI={10.22456/2175-2745.150621}, '
           'abstractNote={<p>Federated Learning (FL) offers an '
           'effective approach to enable collaborative model training across '
           'decentralized medical institutions while preserving patient '
           'privacy. However, FL faces challenges related to data '
           'heterogeneity and inefficient client participation issues. In this '
           'study, we introduce a novel client selection strategy based on '
           'Particle Swarm Optimization (PSO) to improve medical image '
           'classification in federated settings. Our method dynamically '
           'selects informative clients by evaluating their contributions to '
           'the global model, which reduces communication overhead and '
           'supports faster convergence. We performed experiments using three '
           'publicly available medical imaging datasets, Biglycan, ALL-IDB, '
           'and LC25000, focusing on binary classification tasks. The results '
           'show that the PSO-based approach improves performance over random '
           'client selection across all evaluated scenarios, with gains in '
           'accuracy, recall, precision, and F1-Score, even when selecting '
           'fewer clients. These findings indicate that incorporating PSO into '
           'FL frameworks helps address challenges related to data '
           'heterogeneity and communication efficiency while maintaining '
           'classification performance and privacy '
           'preservation.</p>}, number={2}, journal={Revista de '
           'Informática Teórica e Aplicada}, author={Rodrigues, Leonardo '
           'Gabriel Ferreira and Rodrigues Moreira, Larissa Ferreira and '
           'Moreira, Rodrigo and Backes, André Ricardo}, year={2026}, '
           'month={Mar.}, pages={333–340} }',
 'created': '2026-05-22 19:52:29.073981+00:00',
 'created_by': 'e89cc650-904d-40c0-855c-46dedc374691',
 'link': 'https://seer.ufrgs.br/index.php/rita/article/view/150621',
 'modified': '2026-05-22 19:52:29.074010+00:00',
 'modified_by': 'e89cc650-904d-40c0-855c-46dedc374691',
 'project_name': None,
 'project_uuid': None,
 'title': 'PSO-based Client Selection to Improve Medical Image Classification '
          'in Federated Learning',
 'uuid': '907bd5bf-ebb1-42ae-b260-fb0745a46aa6',
 'venue': 'Revista de Informática Teórica e Aplicada',
 'year': '2026'}

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