RT Dissertation/Thesis
T1 Privacy-preserving distributed artificial intelligence in connectionism-based models
A1 Arevalo Barco, Irina
K1 Inteligencia artificial
K1 Aprendizaje federado
K1 Privacidad de los datos
AB Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learning the architecture was a centralized neural network, and the aggregation was done with federated averaging, meaning that a central server will orchestrate the federation using the most straightforward averaging strategy. In this thesis we discuss several advances in aggregation methods, encryption methods to ensure the privacy of the system, study of non-iid datasets, and federation of Fuzzy Cognitive Maps.
YR 2024
FD 2024
LK https://hdl.handle.net/10433/21652
UL https://hdl.handle.net/10433/21652
LA en
NO Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111
NO Universidad Pablo de Olavide. Departamento de Deporte e Informática
DS RIO
RD May 24, 2026