Analysis of strategies to apply federated learning in the field of robotics

  1. Martínez Enríquez, Roi 1
  2. Fraga Corredoira, Nicolás 2
  3. Burés Amatriaín, José Miguel 1
  4. Iglesias Rodríguez, Roberto 1
  5. García Polo, Francisco Javier 2
  6. Fernández Vidal, Xosé Ramón 1
  1. 1 CiTiUS - Universidade de Santiago de Compostela
  2. 2 EPSE - Universidade de Santiago de Compostela
Libro:
Proceedings of the XXIV Workshop of Physical Agents: September 5-6, 2024
  1. Miguel Cazorla (coord.)
  2. Francisco Gomez-Donoso (coord.)
  3. Felix Escalona (coord.)

Editorial: Universidad de Alicante / Universitat d'Alacant

ISBN: 978-84-09-63822-2

Año de publicación: 2024

Páginas: 265-279

Congreso: WAF (24. 2024. Alicante)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Resumen

In this paper we analyse the use of techniques that will help us to apply federated learning in the context of robotics. Federated learning allows the building of a global model from data that is collected locally, at different devices and without data sharing. Nowadays there are already solutions that face the problem of data heterogeneity (non i.i.d). Nevertheless, even when there seem to be a common task that guides the federated learning, the local implementation or details are not exactly identical. This is particularly important in the case of robotics. In this paper we analyse the performance of different techniques that allow us to identify malicious learners, or minority dissenting learners. This identification will allow to modulate the impact of these learners in the global model. Finally, in order to apply federated learning in the context of robotics, we also need strategies which allow the learning of a model when the sensors on the robots are heterogeneous.

Fuente de los datos: Dialnet