Evaluating Federated Learning Scenarios in a Tumor Classification Application

  • Rafaela C. Brum UFF
  • George Teodoro UFMG
  • Lúcia Drummond UFF
  • Luciana Arantes Sorbonne Université
  • Maria Clicia Castro UERJ
  • Pierre Sens Sorbonne Université


Federated Learning is a new area of distributed Machine Learning (ML) that emerged to deal with data privacy concerns. In this approach, each client has access to a local and private dataset. They only exchange the model weights and updates. This paper presents a Federated Learning (FL) approach to a cloud Tumor-Infiltrating Lymphocytes (TIL) application. The results show that the FL approach outperformed the centralized one in all evaluated ML metrics. It also reduced the execution time although the financial cost has increased.


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BRUM, Rafaela C.; TEODORO, George; DRUMMOND, Lúcia; ARANTES, Luciana ; CASTRO, Maria Clicia; SENS, Pierre. Evaluating Federated Learning Scenarios in a Tumor Classification Application. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DO RIO DE JANEIRO (ERAD-RJ), 7. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 6-10. DOI: https://doi.org/10.5753/eradrj.2021.18558.