2025-10-012025-10-012025-09-09SILVA, Isaque Oliveira; SILVA, Carlos Eduardo Vitelli da. SCOPE-FL: seleção de clientes por ordem de entropia no aprendizado federado. Orientador: Iago Lins de Medeiros. 2025. 11 f. Trabalho de Curso (Bacharelado em Engenharia da Computação) – Faculdade de Engenharia da Computação, Campus Universitário de Tucuruí, Universidade Federal do Pará, Tucuruí, 2025. Disponível em: https://bdm.ufpa.br/handle/prefix/8649. Acesso em:.https://bdm.ufpa.br/handle/prefix/8649The increasing use of connected devices requires new methods to handle the quantity and privacy of shared data. Federated Learning (FL) emerges as a solution, enabling model training without directly sharing data, preserving the clients’ privacy. However, not all clients are equally useful for improving global models, making efficient client selection necessary. SCOPE-FL proposes a dynamic client selection mechanism, assigning weights to data entropy and dataset size to ensure a more efficient contribution to the global model. This is done by calculating a relevance score for each client based on these factors and adjusting the weights assigned to each client. SCOPE-FL uses the FedAvg method to aggregate local models, prioritizing clients with more relevant data. Tested with MNIST, SCOPE-FL outperformed traditional methods, showing an accuracy rate of over 60% after 12 rounds, reaching up to 80% after 22 rounds.Acesso AbertoSistemas de informaçãoAprendizagem de máquinaAprendizado federadoSegurança da informaçãoCNPQ::ENGENHARIASCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::SISTEMAS DE INFORMACAOTrabalho de Curso - Graduação - ArtigoAttribution-NonCommercial-NoDerivs 3.0 Brazil