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

• Martínez Enríquez, Roi ^[1] ; Fraga Corredoira, Nicolás ^[1] ; Burés Amatriaín, José Miguel ^[1] ; Iglesias Rodríguez, Roberto ^[1] ; García Polo, Francisco Javier ^[1] ; Fernández Vidal, Xosé Ramón ^[1]
  1. [1] Universidade de Santiago de Compostela

     Universidade de Santiago de Compostela

     Santiago de Compostela, España

     
• Localización: Proceedings of the XXIV Workshop of Physical Agents: September 5-6, 2024 / coord. por Miguel Cazorla Quevedo , Francisco Gómez Donoso , Félix Escalona Moncholi , 2024, ISBN 978-84-09-63822-2, págs. 265-279
• Idioma: inglés
• Enlaces
  • Texto completo
• 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.

• Referencias bibliográficas
  • Wang, Ning and Xiao, Yang and Chen, Yimin and Hu, Yang and Lou, Wenjing and Hou, Y. Thomas: FLARE: Defending Federated Learning against Model...
  • Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong: FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. 2022. arXiv...
  • Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov: How To Backdoor Federated Learning. 2019. arXiv preprint arXiv:1807.00459.
  • Zhang, Zaixi and Cao, Xiaoyu and Jia, Jinyuan and Zhenqiang Gong, Neil: FLDetector: Defending Federated Learning Against Model Poisoning Attacks...
  • Zhang,Lefeng and Zhu, Tianqing and Zhang, Haibin and Xiong,Ping and Zhou, Wanlei: FedRecovery: Differentially Private Machine Unlearning for...
  • Deng, Y. and M. Kamani, M and Mahdavi, M.:Adaptive Personalized Federated Learning. Preprint arXiv:2003.13461. 2020