PSO + FL = PAASO : particle swarm optimization + federated learning = privacy-aware agent swarm optimization
Torra i Reventós, Vicenç (Umeå University. Department of Computing Science)
Galván, Edgar (Maynooth University. Department of Computer Science)
Navarro-Arribas, Guillermo (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)

Fecha:	2022
Resumen:	In this paper, we present an unified framework that encompasses both particle swarm optimization (PSO) and federated learning (FL). This unified framework shows that we can understand both PSO and FL in terms of a function to be optimized by a set of agents but in which agents have different privacy requirements. PSO is the most relaxed case, and FL considers slightly stronger constraints. Even stronger privacy requirements can be considered which will lead to still stronger privacy-preserving solutions. Differentially private solutions as well as local differential privacy/reidentification privacy for agents opinions are the additional privacy models to be considered. In this paper, we discuss this framework and the different privacy-related alternatives. We present experiments that show how the additional privacy requirements degrade the results of the system. To that end, we consider optimization problems compatible with both PSO and FL.
Ayudas:	Ministerio de Ciencia e Innovación PID2021-125962OB-C33
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Differential privacy ; Differentially private social choice ; Federated learning ; Masking ; Particle swarm optimization
Publicado en:	International Journal of Information Security, Vol. 21, Issue 6 (December 2022) , p. 1349-1359, ISSN 1615-5270

DOI: 10.1007/s10207-022-00614-6

11 p, 570.0 KB

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