Mitigating communications threats in decentralized federated learning through moving target defense

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have ye...

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Veröffentlicht in:	Wireless networks 2024-12, Vol.30 (9), p.7407-7421
Hauptverfasser:	Martínez Beltrán, Enrique Tomás, Sánchez Sánchez, Pedro Miguel, López Bernal, Sergio, Bovet, Gérôme, Gil Pérez, Manuel, Martínez Pérez, Gregorio, Huertas Celdrán, Alberto
Format:	Artikel
Sprache:	eng
Schlagworte:	Communication Communications Engineering Communications traffic Computer Communication Networks Configurations Defense Electrical Engineering Encryption Engineering Federated learning Internet of Things IT in Business Learning Machine learning Modules Moving targets Networks Privacy Security Threat models Threats Unmanned aerial vehicles Wireless networks
Online-Zugang:	Volltext
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